Pith. sign in

REVIEW 3 major objections 5 minor 84 references

RDD4D: 4D Attention-Guided Road Damage Detection And Classification

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A 4D attention block improves multi-scale road damage detection on a new diverse dataset.

desk verdict New road-damage dataset and a modest attention-augmented RTMDet that beats baselines on four of its five classes; the dropped Pothole class makes the headline 'overall AP' misleading. read the letter →

arxiv 2501.02822 v1 pith:FWOM2LT7 submitted 2025-01-06 cs.CV cs.AIcs.RO

Asma Alkalbani , Muhammad Saqib , Ahmed Salim Alrawahi , Abbas Anwar , Chandarnath Adak , Saeed Anwar This is my paper · ORCID
classification cs.CVcs.AIcs.RO
keywords roaddamagedetectionobjectattentionmechanismmulti-scalefeaturefusionRTMDetDRDDdatasetcrackclassificationsmartphone
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that a small attention block inserted into the neck of the RTMDet object detector makes road damage detection markedly more accurate, and that a new dataset is needed to test this properly. It introduces DRDD, 1,500 GoPro images in which several damage types (alligator, block, longitudinal, and transverse cracks, plus potholes) appear together in one scene, at varying scales and under occlusions, shadows, and weather changes. On DRDD, the enhanced model reports an overall average precision of 0.446, with an AP for large cracks of 0.458 against 0.280 for the RTMDet baseline. On CrackTinyNet, it reports a mAP at IoU 0.5 of 0.825, about 0.21 above the previous best CrTNet score of 0.601. If these numbers hold, an inexpensive detector with one attention module could make automated road inspection cheaper and more reliable.

What carries the argument

The load-bearing component is the Attention4D block, a lightweight module applied to feature maps in the neck. Each block passes a feature map through a $1\times1$ convolution and batch normalization, splits it into Query, transposed Key, and Value branches, multiplies Query and Key, adds a positional encoding and a 'Talking Head' input, normalizes with softmax, multiplies by the Value branch with a second Talking Head input, and finishes with another $1\times1$ convolution and batch normalization. Two blocks in the top-down path form the chosen configuration, which runs at 26.8 FPS with 38.5 million parameters and yields the reported mAP of 0.446 on DRDD. The ablation tables support the placement choice: top-down placement beats bottom-up placement (0.446 versus 0.412 mAP), and two blocks beat one block, while adding more blocks costs speed.

What would settle it

Retrain RDD4D and the RTMDet baseline from the same codebase, with identical splits, augmentation, epochs, and standard hyperparameters (momentum 0.9, weight decay $5\times10^{-4}$), and compare AP on DRDD and CrackTinyNet; if the gap shrinks to near zero, the gain is not caused by the Attention4D module. A second check is to evaluate on a set that contains small cracks, since DRDD has none and the paper's only per-scale gains are on large objects.

Watch

Extended reading notes

Core claim

The central claim is that Attention4D blocks let a one-stage detector refine features across scales by combining local and global context, and that this is what drives the reported improvements. The authors place two Attention4D blocks in the top-down path of the RTMDet feature pyramid neck, and their scale-specific numbers show the largest gains on large damage instances: on DRDD, AP for large objects is 0.458 versus 0.280 for RTMDet, and overall AP rises from 0.268 to 0.446. On CrackTinyNet, the model reaches 0.825 mAP at IoU 0.5 versus 0.601 for CrTNet, with recall of 0.98, and the authors attribute the gain to the attention module's handling of both fine details and high-level semantic information. The paper also argues DRDD fills a gap in existing road damage datasets, which it says mostly contain single damage types per image, whereas its images average about three damage instances per scene.

Load-bearing premise

The claim collapses if the comparison detectors were not trained under the same, correctly reported protocol, since the paper's own hyperparameters look swapped (weight decay 0.9 and momentum $5\times10^{-4}$) and training details for the baselines are not given.

Editorial extensions

If this is right

  • Road agencies could run this model on smartphone or vehicle footage and get a reliable first pass at locating large cracks, the damage class that dominates DRDD.
  • Because the chosen configuration runs at 26.8 FPS with 38.5 million parameters, it is fast enough for near-real-time inspection on an edge device.
  • If the CrackTinyNet result transfers, the detector would find almost all damage instances (recall 0.98) while keeping precision at 0.81, which in practice means fewer missed repairs and fewer false alarms.
  • The scale-specific results imply that future detectors for this task should report AP separately for large, medium, and small damage, since one number can hide where the gain actually is.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the claimed multi-scale benefit is only tested on two scales, because DRDD has no small objects and the average precision for medium objects is 0.113 versus 0.123 for RTMDet; the attention module's advantage may be specific to large instances.
  • Editorial inference: the reported hyperparameters look swapped (weight decay 0.9, momentum $5\times10^{-4}$), so as written the training configuration is hard to reproduce, and the 0.178 AP gain over RTMDet should be re-checked under a matched protocol before it is attributed to the module.
  • Editorial inference: the CrackTinyNet comparison mixes a change in architecture with a change in dataset and label-assignment protocol, so the 0.21 mAP jump may not isolate the attention module's contribution.
  • Editorial inference: a direct test would ablate the positional encoding and Talking Head components individually; the paper only ablates block placement and count.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces DRDD, a new road damage detection dataset of 1,500 images with five damage types, and proposes RDD4D, an RTMDet-based detector that inserts Attention4D blocks into the feature pyramid neck. The authors report that RDD4D outperforms several YOLO-family baselines on DRDD, particularly for large cracks, and achieves 0.825 mAP@50 on the external CrackTinyNet benchmark versus 0.601 for CrTNet. The paper also includes ablations on attention block placement and count.

Significance. If confirmed, the work contributes a useful diverse dataset and shows that a lightweight attention module can improve multiscale crack detection. The planned release of code, weights, dataset, and results is a concrete reproducibility asset, and the independent CrackTinyNet evaluation provides external grounding for the model's gains. However, the current reporting contains several inconsistencies that must be resolved before the performance claims can be accepted.

major comments (3)
  1. [VI.A, Tables II–IV] The DRDD evaluation omits the Pothole class entirely, although Section III.E defines Potholes as one of the five damage types and Figure 5 reports pothole annotation counts. The 'overall AP' of 0.446 in Table IV equals the mean of the four listed class APs in Table II (0.145+0.900+0.387+0.352)/4 = 0.446, not a five-class mAP. Consequently, the abstract's claim of 'competitive performance with an overall AP of 0.445' is not supported for the full dataset. The authors must either provide the missing Pothole AP and recompute the aggregate metrics, or explicitly restrict the claims to a four-class subset. The inconsistency is compounded by Table IV, where RTMDet is listed with AP=0.268 although the mean of its four class APs in Table II is 0.242, so the aggregate numbers do not align with the per-class table.
  2. [V.A] The training hyperparameters are reported as 'weight decay at 0.9' and 'momentum at 5e-4,' which are inverted relative to conventional optimizer settings (momentum near 0.9, weight decay near 5e-4). Because no training details are given for the baselines, the reader cannot determine whether the reported gains of RDD4D over RTMDet (AP 0.446 vs 0.268) are due to the Attention4D module or to different optimizer settings. Please provide the correct hyperparameters and the training protocols for all compared detectors.
  3. [VI.B, Table V] Table V is captioned as 'AP results for DRDD,' but its rows (D00 Longitudinal Crack, D10 Transverse Crack, D20 Alligator Crack, D40 Pothole, D43 White Line Blur, D44 Cross Walk Blur, D50 Manhole Cover) are the CrackTinyNet classes, not the five DRDD classes. The caption must be corrected, or the table moved to the CrackTinyNet subsection; as published, the mislabeled table undermines confidence in the experimental reporting.
minor comments (5)
  1. [Abstract] The abstract contains the incomplete phrase 'on our proposed'; this should read 'on our proposed dataset.'
  2. [Figure 5] The label 'Traversal' should be 'Transverse.'
  3. [Table IV] The abstract reports an overall AP of 0.445, while Table IV reports 0.446; the values should be reconciled.
  4. [Table II] The caption does not specify which classes are included; add a note clarifying the class set used for the comparison.
  5. [References] YOLOV6 is cited as [68] in the text of Section VI.A and in Table IV, but the bibliography lists it as [75]; please make the citation numbering consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports benchmark measurements of an architectural variant, not a derivation that reduces to its inputs.

full rationale

The paper's central claims are empirical: an Attention4D-augmented RTMDet detector is trained and compared on the proposed DRDD dataset and on the external CrackTinyNet benchmark. None of the reported AP/mAP numbers are derived from an input; they are measured on held-out splits and against published detectors. The loss function and dynamic label assignment are adopted from RTMDet with stated equations, and the Attention4D block is described as an architectural modification; no quantity is defined in terms of the output it is said to predict. The per-class AP table does show that the reported 'overall AP' of 0.446 equals the mean of four class APs, omitting the Pothole class defined in Section III.E, and Section V.A lists 'weight decay at 0.9' and 'momentum at 5e-4', which are inverted relative to standard settings. These are reporting and consistency concerns, not circularity: they do not make the measured result equal to its input by construction. There is no load-bearing self-citation chain and no imported uniqueness theorem. The external CrackTinyNet evaluation provides independent grounding for the method's transferability, so the result is self-contained against an outside benchmark. Score 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims depend on the DRDD annotations being accurate, the COCO metric being the right evaluation, and the training protocol being correctly reported and comparable across baselines. No physical entities are postulated. The free parameters are architecture choices (block count, placement) and optimization settings.

free parameters (3)
  • Number of Attention4D blocks = 2
    Selected by ablation (Table VIII); not derived from theory. Adding 3 or 4 blocks gives negligible gain.
  • Attention4D placement = top-down path
    Chosen after ablation (Table VII); placement affects AP by 0.034.
  • Training momentum and weight decay = reported as 5e-4 and 0.9 respectively but appear swapped
    Section V.A reports 'weight decay at 0.9' and 'momentum at 5e-4', inconsistent with conventional values; if swapped, the actual values would be momentum 0.9, weight decay 5e-4, both affecting optimization.
assumptions (3)
  • domain assumption COCO evaluation metrics (AP, AP50, AP75) are appropriate for road damage detection
    The paper benchmarks all detectors with COCO metrics without justifying that they reflect maintenance priorities.
  • domain assumption DRDD annotations are correct and consistent across 12 annotators
    Section III.D states manual annotation and review but no inter-annotator agreement is reported.
  • domain assumption GoPro video frames are representative of road damage encountered in practice
    Section V.A describes collection from a car-mounted GoPro; no external validation that this covers the distribution of interest.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RDD4D: 4D Attention-Guided Road Damage Detection And Classification." pith.science (2026). https://pith.science/paper/FWOM2LT7

@misc{pith2026250102822,
  author       = {Pith},
  title        = {Pith review of: RDD4D: 4D Attention-Guided Road Damage Detection And Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FWOM2LT7}},
  note         = {Machine review of arXiv:2501.02822}
}
read the original abstract

Road damage detection and assessment are crucial components of infrastructure maintenance. However, current methods often struggle with detecting multiple types of road damage in a single image, particularly at varying scales. This is due to the lack of road datasets with various damage types having varying scales. To overcome this deficiency, first, we present a novel dataset called Diverse Road Damage Dataset (DRDD) for road damage detection that captures the diverse road damage types in individual images, addressing a crucial gap in existing datasets. Then, we provide our model, RDD4D, that exploits Attention4D blocks, enabling better feature refinement across multiple scales. The Attention4D module processes feature maps through an attention mechanism combining positional encoding and "Talking Head" components to capture local and global contextual information. In our comprehensive experimental analysis comparing various state-of-the-art models on our proposed, our enhanced model demonstrated superior performance in detecting large-sized road cracks with an Average Precision (AP) of 0.458 and maintained competitive performance with an overall AP of 0.445. Moreover, we also provide results on the CrackTinyNet dataset; our model achieved around a 0.21 increase in performance. The code, model weights, dataset, and our results are available on \href{https://github.com/msaqib17/Road_Damage_Detection}{https://github.com/msaqib17/Road\_Damage\_Detection}.

Figures

Figures reproduced from arXiv: 2501.02822 by the authors.

Figure 1
Figure 1. Major challenges in road damage detection: Representative examples showing various challenges: (a) weather conditions affecting damage visibility and appearance, (b) occlusions from vehicles partially hiding damage areas, (c) diverse damage patterns requiring robust detection capabilities, (d) motion blur from vehicle movement impacting image quality, and (e) instance-level variations in damage characteristics and (… view at source ↗
Figure 2
Figure 2. Qualitative comparison of road damage detection using different object detection techniques. The columns present [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Our proposed architecture: The flow of feature maps through the neck section of the object detection network. Attention4D blocks (highlighted in bold green) are strategically applied to the feature maps from the backbone, enhancing spatial and channel-wise information. The neck processes these enhanced features through top-down and bottom-up paths, creating a multi-scale feature representation. in both top-down path… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Precision-recall curves comparing different methods for bounding box prediction across all classes in terms of all-area [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: The graph presents a ground-truth analysis of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The graph demonstrates various error types affecting [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

84 extracted references · 46 canonical work pages

  1. [1]

    Deep learning-based road damage detection and classification for multiple countries,

    D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, A. Mraz, T. Kashiyama, and Y . Sekimoto, “Deep learning-based road damage detection and classification for multiple countries,” Automation in Construction , vol. 132, p. 103935, 2021

    2021

  2. [2]

    A deep learning approach for road damage detection from smartphone images,

    A. Alfarrarjeh, D. Trivedi, S. H. Kim, and C. Shahabi, “A deep learning approach for road damage detection from smartphone images,” in 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 5201– 5204

    2018

  3. [3]

    Highway mileage - united states 1990-2020,

    M. Carlier, “Highway mileage - united states 1990-2020,” Apr 2022, accessed: 2022-12-20. [Online]. Available: https://www.statista.com/ statistics/183397/united-states-highway-mileage-since-1990/

    1990

  4. [4]

    Study: Pothole damage costs u.s. drivers $3b a year,

    “Study: Pothole damage costs u.s. drivers $3b a year,” Mar 2016, accessed: 2022-12-15. [Online]. Available: https://www. insurancejournal.com/magazines/mag-features/2016/03/21/401900

    2016

  5. [5]

    The importance of road maintenance,

    G. Malkoc, “The importance of road maintenance,” July 2015, accessed: 2022-12-25. [Online]. Available: https://www.worldhighways.com/wh8/ wh9/wh10/feature/importance-road-maintenance 12

    work page 2015

  6. [6]

    A real-time 3d scanning system for pavement distortion inspection,

    Q. Li, M. Yao, X. Yao, and B. Xu, “A real-time 3d scanning system for pavement distortion inspection,” Measurement Science and Technology, vol. 21, no. 1, p. 015702, 2009

    work page 2009

  7. [7]

    Vibration-based system for pavement condition evaluation,

    B. X. Yu and X. Yu, “Vibration-based system for pavement condition evaluation,” in Applications of advanced technology in transportation , 2006, pp. 183–189

    work page 2006

  8. [8]

    Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network,

    A. Zhang, K. C. Wang, Y . Fei, Y . Liu, C. Chen, G. Yang, J. Q. Li, E. Yang, and S. Qiu, “Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network,” Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 3, pp. 213–229, 2019

    work page 2019

Show all 84 references
  1. [9]

    Road damage detection and classification using deep neural networks with smartphone images,

    H. Maeda, Y . Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection and classification using deep neural networks with smartphone images,” Computer-Aided Civil and Infrastructure Engineer- ing, vol. 33, no. 12, pp. 1127–1141, 2018

    2018

  2. [10]

    Road crack detection using deep convolutional neural network,

    L. Zhang, F. Yang, Y . D. Zhang, and Y . J. Zhu, “Road crack detection using deep convolutional neural network,” in 2016 IEEE international conference on image processing (ICIP) . IEEE, 2016, pp. 3708–3712

    2016

  3. [11]

    Simultaneous traffic sign detection and boundary estimation using convolutional neural network,

    H. S. Lee and K. Kim, “Simultaneous traffic sign detection and boundary estimation using convolutional neural network,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1652–1663, 2018

    2018

  4. [12]

    Bim-based traffic analysis and simulation at road intersection design,

    K. Castañeda, O. Sánchez, R. F. Herrera, E. Pellicer, and H. Porras, “Bim-based traffic analysis and simulation at road intersection design,” Automation in Construction , vol. 131, p. 103911, 2021

    2021

  5. [13]

    Transformers in pedestrian image retrieval and person re-identification in a multi-camera surveillance system,

    M. Tahir and S. Anwar, “Transformers in pedestrian image retrieval and person re-identification in a multi-camera surveillance system,” Applied Sciences, vol. 11, no. 19, p. 9197, 2021

    2021

  6. [14]

    Towards open world object detection,

    K. Joseph, S. Khan, F. S. Khan, and V . N. Balasubramanian, “Towards open world object detection,” in Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, 2021, pp. 5830–5840

    2021

  7. [15]

    Comprehensive performance indicators for road pavement condition assessment,

    P. Marcelino, M. d. Lurdes Antunes, and E. Fortunato, “Comprehensive performance indicators for road pavement condition assessment,” Struc- ture and Infrastructure Engineering , vol. 14, no. 11, pp. 1433–1445, 2018

    2018

  8. [16]

    Detection of multiple road defects for pavement condition assessment,

    S. C. Radopoulou and I. Brilakis, “Detection of multiple road defects for pavement condition assessment,” Eindhoven, The Netherlands, 2015

    2015

  9. [17]

    A fast and adaptive road defect detection approach using computer vision with real time implementation,

    B. Akarsu, M. KARAKÖSE, K. PARLAK, A. Erhan, and A. SARI- MADEN, “A fast and adaptive road defect detection approach using computer vision with real time implementation,” International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, pp. 290–295, 2016

    2016

  10. [18]

    Pavement crack detection using otsu thresholding for image segmentation,

    A. Akagic, E. Buza, S. Omanovic, and A. Karabegovic, “Pavement crack detection using otsu thresholding for image segmentation,” in 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 2018, pp. 1092–1097

    2018

  11. [19]

    Road crack detection using support vector machine (svm) and otsu algorithm,

    Y . Sari, P. B. Prakoso, and A. R. Baskara, “Road crack detection using support vector machine (svm) and otsu algorithm,” in 2019 6th International Conference on Electric Vehicular Technology (ICEVT) . IEEE, 2019, pp. 349–354

    2019

  12. [20]

    Pavement crack detec- tion and analysis for high-grade highway,

    Y . Maode, B. Shaobo, X. Kun, and H. Yuyao, “Pavement crack detec- tion and analysis for high-grade highway,” in 2007 8th International Conference on Electronic Measurement and Instruments . IEEE, 2007, pp. 4–548

    2007

  13. [21]

    N.-D. Hoang, “An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction,” Advances in Civil Engineering, vol. 2018, no. 1, p. 7419058, 2018

    2018

  14. [22]

    Detection and segmentation of cement concrete pavement pothole based on image processing tech- nology,

    M. Gao, X. Wang, S. Zhu, and P. Guan, “Detection and segmentation of cement concrete pavement pothole based on image processing tech- nology,” Mathematical Problems in Engineering , vol. 2020, no. 1, p. 1360832, 2020

    2020

  15. [23]

    Evaluating pavement cracks with bidimensional empirical mode decomposition,

    A. Ayenu-Prah and N. Attoh-Okine, “Evaluating pavement cracks with bidimensional empirical mode decomposition,” EURASIP Journal on Advances in Signal Processing , vol. 2008, pp. 1–7, 2008

    2008

  16. [24]

    Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network,

    A. Zhang, K. C. P. Wang, B. Li, E. Yang, X. Dai, Y . Peng, Y . Fei, Y . Liu, J. Q. Li, and C. Chen, “Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network,” Computer-Aided Civil and Infrastructure Engineering , vol. 32, no. 10, p. ...

  17. [25]

    An approach for the automated extraction of road surface distress from a uav-derived point cloud,

    S. Biçici and M. Zeybek, “An approach for the automated extraction of road surface distress from a uav-derived point cloud,” Automation in Construction , vol. 122, p. 103475, Feb. 2021. [Online]. Available: http://dx.doi.org/10.1016/j.autcon.2020.103475

    2021

  18. [26]

    Road crack detection using deep convolutional neural network,

    L. Zhang, F. Yang, Y . Daniel Zhang, and Y . J. Zhu, “Road crack detection using deep convolutional neural network,” in 2016 IEEE International Conference on Image Processing (ICIP) . IEEE, Sep

    2016

  19. [27]

    Concrete cracks detection based on deep learning image classification,

    W. R. L. d. Silva and D. S. d. Lucena, “Concrete cracks detection based on deep learning image classification,” in The 18th International Conference on Experimental Mechanics , ser. ICEM 2018, vol. 123. MDPI, Jun. 2018, p. 489. [Online]. Available: http://dx.doi.org/10.3390/IC...

    2018 doi

  20. [28]

    Crack-pot: Autonomous road crack and pothole detection,

    S. Anand, S. Gupta, V . Darbari, and S. Kohli, “Crack-pot: Autonomous road crack and pothole detection,” in 2018 Digital Image Computing: Techniques and Applications (DICTA) . IEEE, Dec. 2018. [Online]. Available: http://dx.doi.org/10.1109/DICTA.2018.8615819

    2018

  21. [29]

    Automatic pavement crack detection based on structured prediction with the convolutional neural network,

    Z. Fan, Y . Wu, J. Lu, and W. Li, “Automatic pavement crack detection based on structured prediction with the convolutional neural network,” arXiv preprint arXiv:1802.02208 , 2018

    2018 arXiv

  22. [30]

    Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network,

    H. Nhat-Duc, Q.-L. Nguyen, and V .-D. Tran, “Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network,” Automation in Construction, vol. 94, p. 203–213, Oct. 2018. [Online]. Available: http://dx.doi...

    2018 doi

  23. [31]

    Attention-guided analysis of infrastructure damage with semi-supervised deep learning,

    E. Karaaslan, U. Bagci, and F. N. Catbas, “Attention-guided analysis of infrastructure damage with semi-supervised deep learning,” Automation in Construction , vol. 125, p. 103634, May 2021. [Online]. Available: http://dx.doi.org/10.1016/j.autcon.2021.103634

    2021

  24. [32]

    Automated pixel-level pavement distress detection based on stereo vision and deep learning,

    J. Guan, X. Yang, L. Ding, X. Cheng, V . C. Lee, and C. Jin, “Automated pixel-level pavement distress detection based on stereo vision and deep learning,” Automation in Construction , vol. 129, p. 103788, Sep. 2021. [Online]. Available: http://dx.doi.org/10.1016/j.autcon.2021.103788

    2021

  25. [34]

    Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing,

    G. Singh, D. Bansal, S. Sofat, and N. Aggarwal, “Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing,” Pervasive and Mobile Computing , vol. 40, p. 71–88, Sep. 2017. [Online]. Available: http://dx.doi.org/10.1016/j.pmcj.2017. 06.002

    2017 doi

  26. [35]

    City-wide road distress monitoring with smartphones,

    C. Mertz, S. Varadharajan, S. Jose, K. Sharma, L. Wander, and J. Wang, “City-wide road distress monitoring with smartphones,” in Proceedings of ITS World Congress , 2014, pp. 1–9

    2014

  27. [36]

    Detection and localization of potholes in roadways using smartphones,

    D. A. Casas Avellaneda and J. F. López-Parra, “Detection and localization of potholes in roadways using smartphones,” DYNA, vol. 83, no. 195, p. 156–162, Feb. 2016. [Online]. Available: http://dx.doi.org/10.15446/dyna.v83n195.44919

    2016 doi

  28. [37]

    Road damage detection and classification using deep neural networks with smartphone images,

    H. Maeda, Y . Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection and classification using deep neural networks with smartphone images,” Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 12, p. 1127–1141, Jun. 2018. [Online]. Available: http:/...

    2018 doi

  29. [38]

    Angulo, J

    A. Angulo, J. A. Vega-Fernández, L. M. Aguilar-Lobo, S. Natraj, and G. Ochoa-Ruiz, Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management . Springer International Publishing, 2019, p. 3–14. [Online]. Available: http://dx.doi.org/10...

    2019 doi

  30. [39]

    Towards low-cost pavement condition health monitoring and analysis using deep learning,

    R. Roberts, G. Giancontieri, L. Inzerillo, and G. Di Mino, “Towards low-cost pavement condition health monitoring and analysis using deep learning,” Applied Sciences, vol. 10, no. 1, p. 319, Jan. 2020. [Online]. Available: http://dx.doi.org/10.3390/app10010319

    2020 doi

  31. [40]

    Pavement distress detection and classification based on yolo network,

    Y . Du, N. Pan, Z. Xu, F. Deng, Y . Shen, and H. Kang, “Pavement distress detection and classification based on yolo network,” International Journal of Pavement Engineering , vol. 22, no. 13, p. 1659–1672, Jan. 2020. [Online]. Available: http://dx.doi.org/10.1080/ 10298436.202...

    2020

  32. [41]

    Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses,

    H. Majidifard, P. Jin, Y . Adu-Gyamfi, and W. G. Buttlar, “Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses,” Transportation Research Record: Journal of the Transportation Research Board , vol. 2674, no. 2, p. 328–339, Feb

  33. [42]

    Potspot: Participatory sensing based monitoring system for pothole detection using deep learning,

    S. Patra, A. I. Middya, and S. Roy, “Potspot: Participatory sensing based monitoring system for pothole detection using deep learning,” Multimedia Tools and Applications , Apr. 2021. [Online]. Available: http://dx.doi.org/10.1007/s11042-021-10874-4

    2021 doi

  34. [43]

    Deep learning-based road damage detection and classification for multiple countries,

    D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, A. Mraz, T. Kashiyama, and Y . Sekimoto, “Deep learning-based road damage detection and classification for multiple countries,” Automation in Construction, vol. 132, p. 103935, Dec. 2021. [Online]. Available: http://dx.doi.org/10.1...

    2021

  35. [45]

    Crackit: An image processing toolbox for crack detection and characterization,

    H. Oliveira and P. L. Correia, “Crackit: An image processing toolbox for crack detection and characterization,” in 2014 IEEE International Conference on Image Processing (ICIP) . IEEE, Oct. 2014. [Online]. Available: http://dx.doi.org/10.1109/ICIP.2014.7025160

    2014

  36. [46]

    Automatic road crack detection using random structured forests,

    Y . Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic road crack detection using random structured forests,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, p. 3434–3445, Dec

  37. [47]

    Cracktree: Automatic crack detection from pavement images,

    Q. Zou, Y . Cao, Q. Li, Q. Mao, and S. Wang, “Cracktree: Automatic crack detection from pavement images,” Pattern Recognition Letters, vol. 33, no. 3, p. 227–238, Feb. 2012. [Online]. Available: http://dx.doi.org/10.1016/j.patrec.2011.11.004

    2012 doi

  38. [48]

    Segment-based pavement crack quantification,

    X. Weng, Y . Huang, and W. Wang, “Segment-based pavement crack quantification,” Automation in Construction , vol. 105, p. 102819, Sep

  39. [49]

    Sdnet2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks,

    S. Dorafshan, R. J. Thomas, and M. Maguire, “Sdnet2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks,” Data in Brief , vol. 21, p. 1664–1668, Dec. 2018. [Online]. Available: http://dx.doi.org/10.1016/j. dib.2018.11.015

    2018 doi

  40. [50]

    Available: http://dx.doi.org/10.1109/TITS.2016.2552248

    [Online]. Available: http://dx.doi.org/10.1109/TITS.2016.2552248

    2016

  41. [51]

    Feature pyramid and hierarchical boosting network for pavement crack detection,

    F. Yang, L. Zhang, S. Yu, D. Prokhorov, X. Mei, and H. Ling, “Feature pyramid and hierarchical boosting network for pavement crack detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, p. 1525–1535, Apr. 2020. [Online]. Available: http://dx.doi.o...

    2020

  42. [52]

    A cost effective solution for pavement crack inspection using cameras and deep neural networks,

    Q. Mei and M. Gül, “A cost effective solution for pavement crack inspection using cameras and deep neural networks,” Construction and Building Materials, vol. 256, p. 119397, Sep. 2020. [Online]. Available: http://dx.doi.org/10.1016/j.conbuildmat.2020.119397

    2020

  43. [53]

    How to get pavement distress detection ready for deep learning? a systematic approach,

    M. Eisenbach, R. Stricker, D. Seichter, K. Amende, K. Debes, M. Sesselmann, D. Ebersbach, U. Stoeckert, and H.-M. Gross, “How to get pavement distress detection ready for deep learning? a systematic approach,” in 2017 International Joint Conference on Neural Networks (IJCNN) ....

    2017

  44. [54]

    Improving visual road condition assessment by extensive experiments on the extended gaps dataset,

    R. Stricker, M. Eisenbach, M. Sesselmann, K. Debes, and H.-M. Gross, “Improving visual road condition assessment by extensive experiments on the extended gaps dataset,” in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, Jul. 2019. [Online]. Available: ht...

    2019

  45. [55]

    Automatic pavement crack recognition based on bp neural network,

    L. Li, L. Sun, G. Ning, and S. Tan, “Automatic pavement crack recognition based on bp neural network,” PROMET - Traffic&Transportation, vol. 26, no. 1, p. 11–22, Jan. 1970. [Online]. Available: http://dx.doi.org/10.7307/ptt.v26i1.1477

    1970 doi

  46. [56]

    Beyond bounding- box: Convex-hull feature adaptation for oriented and densely packed object detection,

    Z. Guo, C. Liu, X. Zhang, J. Jiao, X. Ji, and Q. Ye, “Beyond bounding- box: Convex-hull feature adaptation for oriented and densely packed object detection,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2021, pp. 8792–8801

    2021

  47. [57]

    Feature pyramid networks for object detection,

    T.-Y . Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125

    2017

  48. [58]

    Path aggregation network for instance segmentation,

    S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8759–8768

    2018

  49. [59]

    Conditional convolutions for instance segmentation,

    Z. Tian, C. Shen, and H. Chen, “Conditional convolutions for instance segmentation,” in Computer Vision–ECCV 2020: 16th European Con- ference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 . Springer, 2020, pp. 282–298

    2020

  50. [60]

    Yolov4: Op- timal speed and accuracy of object detection,

    A. Bochkovskiy, C.-Y . Wang, and H.-Y . M. Liao, “Yolov4: Op- timal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020

    2004 arXiv

  51. [61]

    Focal loss for dense object detection,

    T. Lin, “Focal loss for dense object detection,” arXiv preprint arXiv:1708.02002, 2017

    2017 arXiv

  52. [62]

    Fully convolutional one-stage 3d object detection on lidar range images,

    Z. Tian, X. Chu, X. Wang, X. Wei, and C. Shen, “Fully convolutional one-stage 3d object detection on lidar range images,”Advances in Neural Information Processing Systems , vol. 35, pp. 34 899–34 911, 2022

    2022

  53. [63]

    End-to-end object detection with transformers,

    N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision . Springer, 2020, pp. 213– 229

    2020

  54. [64]

    Ota: Optimal transport assignment for object detection,

    Z. Ge, S. Liu, Z. Li, O. Yoshie, and J. Sun, “Ota: Optimal transport assignment for object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 303– 312

    2021

  55. [65]

    Tood: Task- aligned one-stage object detection,

    C. Feng, Y . Zhong, Y . Gao, M. R. Scott, and W. Huang, “Tood: Task- aligned one-stage object detection,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, 2021, pp. 3490–3499

    2021

  56. [66]

    Rtmdet: An empirical study of designing real-time object detectors,

    C. Lyu, W. Zhang, H. Huang, Y . Zhou, Y . Wang, Y . Liu, S. Zhang, and K. Chen, “Rtmdet: An empirical study of designing real-time object detectors,” 2022

    2022

  57. [67]

    Ultralytics yolov8,

    G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

    2023

  58. [68]

    YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

    C.-Y . Wang, A. Bochkovskiy, and H.-Y . M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv preprint arXiv:2207.02696 , 2022

    2022 arXiv

  59. [69]

    Pp-yoloe: An evolved version of yolo,

    S. Xu, X. Wang, W. Lv, Q. Chang, C. Cui, K. Deng, G. Wang, Q. Dang, S. Wei, Y . Du, and B. Lai, “Pp-yoloe: An evolved version of yolo,” ArXiv, vol. abs/2203.16250, 2022

    2022 arXiv

  60. [71]

    Generalized focal loss: Towards efficient representation learning for dense object detec- tion,

    X. Li, C. Lv, W. Wang, G. Li, L. Yang, and J. Yang, “Generalized focal loss: Towards efficient representation learning for dense object detec- tion,” IEEE transactions on pattern analysis and machine intelligence , vol. 45, no. 3, pp. 3139–3153, 2022

    2022

  61. [72]

    Generalized intersection over union: A metric and a loss for bounding box regression,

    H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” in Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , 2019, pp. 658–666

    2019

  62. [73]

    Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,

    S. Zhang, C. Chi, Y . Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9759–9768

    2020

  63. [74]

    Ultralytics yolov8,

    G. Jocher, “Ultralytics yolov8,” https://github.com/ultralytics/ultralytics, 2023, accessed: [Insert access date]

    2023

  64. [75]

    YOLOX: Exceeding yolo series in 2021,

    Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding yolo series in 2021,” arXiv preprint arXiv:2107.08430 , 2021

    2021 arXiv

  65. [76]

    Cracktinynet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection,

    H. Li, T. Peng, N. Qiao, Z. Guan, X. Feng, P. Guo, T. Duan, and J. Gong, “Cracktinynet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection,” IET Intelligent Transport Systems , vol. 18, no. 12, pp. 2693–2712,

  66. [77]

    Efficientdet: Scalable and efficient object detection,

    M. Tan, R. Pang, and Q. V . Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 781–10 790

    2020

  67. [78]

    ultralytics/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations,

    G. Jocher, A. Chaurasia, A. Stoken, J. Borovec, NanoCode012, Y . Kwon, TaoXie, K. Michael, J. Fang, imyhxy, Lorna, C. Wong, Z. Yifu, A. V , D. Montes, Z. Wang, C. Fati, J. Nadar, Laughing, UnglvKitDe, tkianai, yxNONG, P. Skalski, A. Hogan, M. Strobel, M. Jain, L. Mammana, and ...

  68. [79]

    Ssd: Single shot multibox detector,

    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y . Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision– ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 . Springer, 2016, pp. 21–37

    2016

  69. [80]

    Yolov6: A single-stage object detection framework for industrial applications,

    C. Li, L. Li, H. Jiang, K. Weng, Y . Geng, L. Li, Z. Ke, Q. Li, M. Cheng, W. Nie et al. , “Yolov6: A single-stage object detection framework for industrial applications,” arXiv preprint arXiv:2209.02976 , 2022

    2022 arXiv

  70. [87]

    Faster r-cnn: Towards real-time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1137–1149, 2016

    2016

  71. [2016]

    Available: http://dx.doi.org/10.1109/ICIP.2016.7533052

    [Online]. Available: http://dx.doi.org/10.1109/ICIP.2016.7533052

    2016

  72. [2017]

    Available: http://dx.doi.org/10.1111/mice.12297

    [Online]. Available: http://dx.doi.org/10.1111/mice.12297

    doi

  73. [2019]

    Available: http://dx.doi.org/10.1016/j.autcon.2019.04

    [Online]. Available: http://dx.doi.org/10.1016/j.autcon.2019.04. 014

    2019 doi

  74. [2020]

    Available: http://dx.doi.org/10.1177/0361198120907283

    [Online]. Available: http://dx.doi.org/10.1177/0361198120907283

    doi

  75. [2022]

    Available: https://doi.org/10.5281/zenodo.7002879

    [Online]. Available: https://doi.org/10.5281/zenodo.7002879

    doi

  76. [2024]

    Available: https://ietresearch.onlinelibrary.wiley.com/ doi/abs/10.1049/itr2.12497

    [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/ doi/abs/10.1049/itr2.12497

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.