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REVIEW 3 major objections 4 minor 35 references

X-ray illicit object detection using hybrid CNN-transformer neural network architectures

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper finds that when X-ray images come from different scanners, hybrid CNN-transformer detectors outperform a pure CNN baseline, while the CNN remains best on single-scanner datasets.

desk verdict A useful empirical benchmark for X-ray object detection, but the headline robustness claim rests on single-run gaps that need error bars before it is established. read the letter →

arxiv 2505.00564 v1 pith:NZYZJK5P submitted 2025-05-01 cs.CV

Jorgen Cani , Christos Diou , Spyridon Evangelatos , Panagiotis Radoglou-Grammatikis , Vasileios Argyriou , Panagiotis Sarigiannidis , Iraklis Varlamis , Georgios Th. Papadopoulos This is my paper · ORCID
classification cs.CV
keywords X-raysecurityimagingobjectdetectionCNN-transformerhybriddomainshiftvisiontransformerNext-ViTYOLOv8
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 asks whether combining convolutional and transformer components helps detect illicit objects in X-ray luggage scans, and reports a conditional yes. On two single-scanner benchmarks, a pure CNN detector (YOLOv8 with CSP-DarkNet53) remains the strongest. On EDS, where images come from three different X-ray scanners and models are trained on one scanner and tested on another, hybrid CNN-transformer detectors outperform the CNN baseline. The paper interprets this as transformer components providing increased robustness to domain distribution shift, and argues that X-ray security research should therefore evaluate architectures under multi-scanner conditions rather than single-scanner benchmarks alone.

What carries the argument

The key object is the hybrid CNN-transformer backbone Next-ViT-S, which alternates Next Convolutional Blocks (local detail) with Next Transformer Blocks (global attention) in a pyramidal architecture, and is joined either to the YOLOv8 head or the RT-DETR head. Integrating it requires selecting which backbone layers feed the neck via skip connections; the paper tries C(10,20), C(9,19), and C(7,17), choosing C(7,17) for YOLOv8 and C(9,19) for RT-DETR. The EDS benchmark supplies the load-bearing condition: six train-on-one-scanner/test-on-another sessions that turn the architecture comparison into a robustness comparison.

What would settle it

Retrain all four detectors on EDS under identical optimizer, learning-rate schedule, and training length, then rerun the six cross-scanner sessions; if the pure CNN baseline then matches or beats the hybrids, the claimed robustness advantage is not caused by transformer components.

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Extended reading notes

Core claim

The paper's central claim is that hybrid CNN-transformer detectors exhibit increased robustness to domain distribution shift in X-ray images. The evidence is a four-way comparison: a pure CNN baseline (YOLOv8 with CSP-DarkNet53), a hybrid backbone in YOLOv8 (D(YOLOv8, Next-ViT-S)), and two RT-DETR variants (with HGNetV2 and Next-ViT-S). On HiXray and PIDray, which each come from a single scanner, the CNN baseline performs best. On EDS, whose images were captured by three different scanners and whose protocol trains on one scanner and tests on another, the hybrid detectors outperform the CNN baseline. The paper attributes the difference to transformer components' ability to incorporate global contextual information, while cautioning that swapping in a hybrid backbone does not always improve results.

Load-bearing premise

The robustness conclusion assumes the four detectors were trained fairly, and the most exposed point is that on EDS one hybrid (D(RT-DETR, Next-ViT-S)) used SGD while the others used AdamW, so optimizer choice rather than transformer components could explain part of the gap.

Editorial extensions

If this is right

  • On single-scanner X-ray benchmarks, a well-tuned pure CNN detector remains a strong default; replacing the backbone with a hybrid one does not automatically improve accuracy.
  • When deployment spans multiple scanner types, the paper's results make hybrid detectors, especially D(YOLOv8, Next-ViT-S) and D(RT-DETR, HGNetV2), the safer choice.
  • YOLOv8-based detectors with the Next-ViT-S backbone consistently outperform their RT-DETR-based counterparts, suggesting the transformer-based detection head is not what drives the robustness gain.
  • On the hidden-object subset of PIDray, the hybrid D(YOLOv8, Next-ViT-S) slightly outperforms the CNN baseline, hinting that global context helps when objects are deliberately concealed.
  • The hybrids' EDS advantage is most visible on medium-sized objects, which is a useful detail for designing screening systems that must handle varied object scales.

Reading between the lines

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

  • If the EDS advantage persists under matched optimizers, it would suggest that global context from self-attention is what buys robustness to scanner-induced appearance shifts, making multi-scanner evaluation a standard part of X-ray detector benchmarking.
  • A natural testable extension is to measure how the hybrid advantage scales with the severity of the shift, for example by adding more scanners, varied wear levels, or simulated color and contrast perturbations to single-scanner datasets.
  • The paper's skip-connection search implies that how a hybrid backbone is wired to the detection neck can matter as much as the backbone itself, so a systematic study of those connection choices across datasets could turn a heuristic into a design rule.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper compares four object detectors on three public X-ray benchmarks: a pure CNN baseline D(YOLOv8, CSP-DarkNet53), a hybrid backbone D(YOLOv8, Next-ViT-S), a CNN backbone with transformer head D(RT-DETR, HGNetV2), and a fully hybrid D(RT-DETR, Next-ViT-S). The authors report that the CNN baseline is best on HiXray and PIDray (single-scanner datasets), while two of the hybrid architectures achieve higher mAP on EDS, which contains images from three scanners and is used with a cross-domain evaluation protocol. The paper interprets this as evidence that transformer components confer increased robustness to domain shift. Tables I–III provide mAP50 and mAP50:95 for all datasets and for the EDS domain pairs and PIDray subsets.

Significance. If the central claim is substantiated, the result is practically useful: multi-scanner X-ray deployments are common, and a detector class that is systematically more robust under domain shift would be a valuable design guideline. The paper also provides a reproducible benchmark setup, with public datasets, source code, and network weights released, and it reports per-class and per-scale breakdowns that are useful for future work. The tables are internally consistent, and the EDS session averages in Table II reproduce the aggregate numbers in Table I. However, the headline interaction claim—that hybrid CNN-transformer architectures are more robust under domain shift—is currently supported only by single-run point estimates with no variance or significance assessment, which is the central weakness of the study.

major comments (3)
  1. [Section IV-B, Tables I and II] The central robustness interaction is derived from single training runs. The EDS advantage of D(YOLOv8, Next-ViT-S) over the baseline is 0.041 mAP50 and 0.022 mAP50:95, and that of D(RT-DETR, HGNetV2) is 0.026 mAP50 and 0.024 mAP50:95. These gaps are comparable to typical seed-to-seed variation in object detection experiments, yet the paper reports no standard deviations, confidence intervals, or significance tests. The six EDS sessions are different source-target domain pairs, not repeated runs of the same training procedure, so they do not estimate seed sensitivity. Without repeated seeds, the claim that transformer components increase robustness under domain shift is not empirically established. Please provide results averaged over at least three seeds (with standard deviations), or a formal significance test, for all detectors and datasets.
  2. [Abstract and Section V] The abstract and conclusion state that 'hybrid CNN-transformer architectures exhibit increased robustness' on EDS, but Table I shows that the hybrid D(RT-DETR, Next-ViT-S) is substantially worse than the CNN baseline on EDS (0.504 vs 0.547 mAP50; 0.322 vs 0.386 mAP50:95). Section IV-B correctly hedges with 'most hybrid CNN-transformer detectors', but the broader claim is contradicted by this counterexample. Please qualify the conclusion to name the specific architectures that show the effect, or provide an explanation for why one hybrid fails while the other two succeed.
  3. [Section III-B, Section III-C, and Section IV-A3] The skip-connection configurations C(7,17) for D(YOLOv8, Next-ViT-S) and C(9,19) for D(RT-DETR, Next-ViT-S) are described as selected after 'extensive experimental evaluation' and 'thorough experimental assessment', but no validation procedure is reported. If these configurations were chosen using the test sets or the same EDS sessions as the final evaluation, the reported numbers are the best of several tested variants and the hybrid advantage could be inflated by selection. Please specify the hyperparameter/model-selection protocol: which data splits were used for configuration selection, and were the chosen configurations frozen before the final evaluation on the test sets?
minor comments (4)
  1. [Section IV-A3] The optimizer choice is inconsistent: on EDS, D(RT-DETR, Next-ViT-S) is trained with SGD while all other detectors use AdamW, and on HiXray/PIDray all detectors use SGD. The main EDS comparison for the two supporting hybrids is unaffected, but the cross-dataset comparison and the comparison involving D(RT-DETR, Next-ViT-S) are confounded. Please justify or unify the optimizer settings.
  2. [Section IV-B] The sentence 'the various subsets in the PID dataset' appears to contain a typo; it should read 'PIDray dataset'.
  3. [Section III-B and III-C] The layer numbering for the Next-ViT-S backbone (C(10,20), C(9,19), C(7,17)) is used without a list of which layer indices correspond to NCB versus NTB blocks in the main text; a small table or figure callout would improve reproducibility.
  4. [Section IV-B, Figure 4] The discussion of object-scale results states that the scale effect differs on EDS but does not describe the HiXray and PIDray scale results in the same detail; adding one or two sentences would make the figure interpretation self-contained.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports an empirical benchmark against external public datasets, and its central claim is an observation from those results, not a constructed prediction.

full rationale

The central claim, that hybrid CNN-transformer detectors exhibit increased robustness under domain shift on EDS, is an empirical observation from Tables I and II, which report mAP values on the external public EDS, HiXray, and PIDray benchmarks. No parameter is fitted to a subset of data and then relabeled as a prediction; all four detectors are trained with standard public implementations and evaluated on fixed public test protocols. The selected skip-connection configurations C(7,17) and C(9,19) are architecture variants chosen by experimentation, which is model selection rather than circular reasoning, and the claim is also supported by D(RT-DETR, HGNetV2), which does not depend on those skip connections. The paper's self-citations (e.g., [6], [10], [15], [31]) appear only as background context and do not carry the load of the empirical comparison. The absence of repeated-seed variance or significance testing is an evidence-strength concern, not a circularity concern, so it does not affect the circularity score.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new mathematical framework or entities. Its only fitted choice is the skip-connection configuration, which is a categorical architecture selection rather than a continuous free parameter. The main assumptions are domain assumptions about transfer from ImageNet and the validity of COCO scale definitions for X-ray images.

free parameters (1)
  • Skip-connection configuration for Next-ViT-S hybrids = C(7,17) for YOLOv8 head, C(9,19) for RT-DETR head
    Chosen after 'extensive experimental evaluation' on the same datasets (Sections III-B and III-C). This is a model selection step that may inflate performance on the test sets if not validated on held-out data.
assumptions (3)
  • domain assumption ImageNet-pretrained weights for Next-ViT-S transfer to X-ray imagery
    Section III-A uses ImageNet-pretrained weights without domain-specific pretraining. If transfer is poor, comparisons with CNN backbones may be unfair.
  • domain assumption COCO small/medium/large object scale thresholds apply to X-ray images
    Section IV-B uses COCO scale definitions for the object-size analysis. X-ray imaging distances and object sizes differ from natural images, so the thresholds may not be meaningful.
  • ad hoc to paper EDS domain-shift protocol is a valid proxy for real-world distribution shift
    The paper generalizes from six scanner-pair sessions to 'hybrid CNNs are more robust'. This assumes the EDS sessions capture the kind of shift found in real deployments.

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Cite this review

Pith. "Pith review of X-ray illicit object detection using hybrid CNN-transformer neural network architectures." pith.science (2026). https://pith.science/paper/NZYZJK5P

@misc{pith2026250500564,
  author       = {Pith},
  title        = {Pith review of: X-ray illicit object detection using hybrid CNN-transformer neural network architectures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZYZJK5P}},
  note         = {Machine review of arXiv:2505.00564}
}
read the original abstract

In the field of X-ray security applications, even the smallest details can significantly impact outcomes. Objects that are heavily occluded or intentionally concealed pose a great challenge for detection, whether by human observation or through advanced technological applications. While certain Deep Learning (DL) architectures demonstrate strong performance in processing local information, such as Convolutional Neural Networks (CNNs), others excel in handling distant information, e.g., transformers. In X-ray security imaging the literature has been dominated by the use of CNN-based methods, while the integration of the two aforementioned leading architectures has not been sufficiently explored. In this paper, various hybrid CNN-transformer architectures are evaluated against a common CNN object detection baseline, namely YOLOv8. In particular, a CNN (HGNetV2) and a hybrid CNN-transformer (Next-ViT-S) backbone are combined with different CNN/transformer detection heads (YOLOv8 and RT-DETR). The resulting architectures are comparatively evaluated on three challenging public X-ray inspection datasets, namely EDS, HiXray, and PIDray. Interestingly, while the YOLOv8 detector with its default backbone (CSP-DarkNet53) is generally shown to be advantageous on the HiXray and PIDray datasets, when a domain distribution shift is incorporated in the X-ray images (as happens in the EDS datasets), hybrid CNN-transformer architectures exhibit increased robustness. Detailed comparative evaluation results, including object-level detection performance and object-size error analysis, demonstrate the strengths and weaknesses of each architectural combination and suggest guidelines for future research. The source code and network weights of the models employed in this study are available at https://github.com/jgenc/xray-comparative-evaluation.

Figures

Figures reproduced from arXiv: 2505.00564 by the authors.

Figure 1
Figure 1. Architecture of the D(YOLOv8, Next-ViT-S) detec [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Architecture of the D(RT-DETR, Next-ViT-S) detec [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Object-level performance (mAP50:95 metric) for datasets: a) EDS, b) HiXray, and c) PIDray. of X-ray security images, which inherently exhibit no particular spatial structure (i.e. objects in containers are usually positioned without a specific/consistent spatial order). • Replacing a CNN backbone (either CSP-DarkNet53 and HGNetV2) with a hybrid one (Next-ViT-S) is shown not to always lead to improved performance (Ta… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Indicative object detection results in the EDS, HiXray, and PIDray datasets. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Works this paper leans on

35 extracted references · 27 canonical work pages

  1. [1]

    Pidray: A large-scale x- ray benchmark for real-world prohibited item detection,

    L. Zhang, L. Jiang, R. Ji, and H. Fan, “Pidray: A large-scale x- ray benchmark for real-world prohibited item detection,” International Journal of Computer Vision , vol. 131, no. 12, pp. 3170–3192, 2023

    work page 2023

  2. [2]

    Illicit item detection in x-ray images for security applications,

    G. Batsis, I. Mademlis, and G. T. Papadopoulos, “Illicit item detection in x-ray images for security applications,” in 2023 IEEE Ninth Inter- national Conference on Big Data Computing Service and Applications (BigDataService), pp. 63–70, IEEE, 2023

    work page 2023

  3. [3]

    Computer vision on x-ray data in industrial production and security applications: A comprehensive survey,

    M. Rafiei, J. Raitoharju, and A. Iosifidis, “Computer vision on x-ray data in industrial production and security applications: A comprehensive survey,” Ieee Access, vol. 11, pp. 2445–2477, 2023

    work page 2023

  4. [4]

    Visual inspection for illicit items in x-ray images using deep learn- ing,

    I. Mademlis, G. Batsis, A. A. R. Chrysochoou, and G. T. Papadopoulos, “Visual inspection for illicit items in x-ray images using deep learn- ing,” in 2023 IEEE International Conference on Big Data (BigData) , pp. 4081–4089, IEEE, 2023

    work page 2023

  5. [5]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

    arXiv 2010

  6. [6]

    Illicit object detection in x-ray images using vision transformers,

    J. Cani, I. Mademlis, A. A. R. Chrysochoou, and G. T. Papadopoulos, “Illicit object detection in x-ray images using vision transformers,” in 2024 5th International Conference in Electronic Engineering, Informa- tion Technology & Education (EEITE) , pp. 1–6, IEEE, 2024

    work page 2024

  7. [7]

    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 , pp. 213–229, Springer, 2020

    2020

  8. [8]

    Sparse detr: Efficient end-to-end object detection with learnable sparsity,

    B. Roh, J. Shin, W. Shin, and S. Kim, “Sparse detr: Efficient end-to-end object detection with learnable sparsity,” arXiv preprint arXiv:2111.14330, 2021

    arXiv 2021

Show all 35 references
  1. [9]

    Dino: Detr with improved denoising anchor boxes for end-to- end object detection,

    H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. M. Ni, and H.-Y . Shum, “Dino: Detr with improved denoising anchor boxes for end-to- end object detection,” arXiv preprint arXiv:2203.03605 , 2022

    2022 arXiv

  2. [10]

    Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions,

    P. Alimisis, I. Mademlis, P. Radoglou-Grammatikis, P. Sarigiannidis, and G. T. Papadopoulos, “Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions,” Artificial Intelligence Review, vol. 58, no....

    2025

  3. [11]

    A survey of the vision transformers and their cnn-transformer based variants,

    A. Khan, Z. Rauf, A. Sohail, A. R. Khan, H. Asif, A. Asif, and U. Fa- rooq, “A survey of the vision transformers and their cnn-transformer based variants,” Artificial Intelligence Review , vol. 56, no. Suppl 3, pp. 2917–2970, 2023

    2023

  4. [12]

    Combining transformer and cnn for object detection in uav imagery,

    W. F. Hendria, Q. T. Phan, F. Adzaka, and C. Jeong, “Combining transformer and cnn for object detection in uav imagery,” ICT Express, vol. 9, no. 2, pp. 258–263, 2023

    2023

  5. [13]

    Next-ViT: Next generation vision transformer for ef- ficient deployment in realistic industrial scenarios,

    J. Li, X. Xia, W. Li, H. Li, X. Wang, X. Xiao, R. Wang, M. Zheng, and X. Pan, “Next-ViT: Next generation vision transformer for ef- ficient deployment in realistic industrial scenarios,” arXiv preprint arXiv:2207.05501, 2022

    2022 arXiv

  6. [14]

    Object detection and x-ray security imaging: A survey,

    J. Wu, X. Xu, and J. Yang, “Object detection and x-ray security imaging: A survey,” IEEE Access, vol. 11, pp. 45416–45441, 2023

    2023

  7. [15]

    The invisible arms race: digital trends in illicit goods trafficking and ai-enabled responses,

    I. Mademlis, M. Mancuso, C. Paternoster, S. Evangelatos, E. Finlay, J. Hughes, P. Radoglou-Grammatikis, P. Sarigiannidis, G. Stavropoulos, K. V otis,et al., “The invisible arms race: digital trends in illicit goods trafficking and ai-enabled responses,” IEEE Transactions on Te...

    2024

  8. [16]

    Baidu Paddle Vision Team, HGNetv2, 2023

    2023

  9. [17]

    Jocher, J

    G. Jocher, J. Qiu, and A. Chaurasia, Ultralytics YOLO, Jan. 2023

    2023

  10. [18]

    Detrs beat yolos on real-time object detection,

    Y . Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y . Liu, and J. Chen, “Detrs beat yolos on real-time object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 16965–16974, 2024

    2024

  11. [19]

    Exploring endogenous shift for cross-domain detection: A large-scale benchmark and perturbation suppression network,

    R. Tao, H. Li, T. Wang, Y . Wei, Y . Ding, B. Jin, H. Zhi, X. Liu, and A. Liu, “Exploring endogenous shift for cross-domain detection: A large-scale benchmark and perturbation suppression network,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), ...

    2022

  12. [20]

    Towards real-world x-ray security inspection: A high-quality benchmark and lateral inhibition module for prohibited items detection,

    R. Tao, Y . Wei, X. Jiang, H. Li, H. Qin, J. Wang, Y . Ma, L. Zhang, and X. Liu, “Towards real-world x-ray security inspection: A high-quality benchmark and lateral inhibition module for prohibited items detection,” in Proceedings of the IEEE/CVF international conference on co...

    2021

  13. [21]

    Towards real- world prohibited item detection: A large-scale x-ray benchmark,

    B. Wang, L. Zhang, L. Wen, X. Liu, and Y . Wu, “Towards real- world prohibited item detection: A large-scale x-ray benchmark,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 5412–5421, 2021

    2021

  14. [22]

    Occluded prohibited items detection: An x-ray security inspection benchmark and de-occlusion attention module,

    Y . Wei, R. Tao, Z. Wu, Y . Ma, L. Zhang, and X. Liu, “Occluded prohibited items detection: An x-ray security inspection benchmark and de-occlusion attention module,” in Proceedings of the 28th ACM international conference on multimedia , pp. 138–146, 2020

    2020

  15. [23]

    Em-yolo: An x-ray prohibited- item-detection method based on edge and material information fusion,

    B. Jing, P. Duan, L. Chen, and Y . Du, “Em-yolo: An x-ray prohibited- item-detection method based on edge and material information fusion,” Sensors, vol. 23, no. 20, p. 8555, 2023

    2023

  16. [24]

    Sc-yolov8: A security check model for the inspection of prohibited items in x-ray images,

    L. Han, C. Ma, Y . Liu, J. Jia, and J. Sun, “Sc-yolov8: A security check model for the inspection of prohibited items in x-ray images,” Electronics, vol. 12, no. 20, p. 4208, 2023

    2023

  17. [25]

    Lightweight detection method for x-ray security inspection with occlusion,

    Z. Wang, X. Wang, Y . Shi, H. Qi, M. Jia, and W. Wang, “Lightweight detection method for x-ray security inspection with occlusion,” Sensors, vol. 24, no. 3, p. 1002, 2024

    2024

  18. [26]

    Improved yolov8 for dangerous goods detection in x-ray security images,

    A. Wang, P. Yuan, H. Wu, Y . Iwahori, and Y . Liu, “Improved yolov8 for dangerous goods detection in x-ray security images,” Electronics, vol. 13, no. 16, p. 3238, 2024

    2024

  19. [27]

    Lightweight prohibited items detection model in x-ray images based on improved yolov7-tiny,

    H. Zhang, W. Teng, X. He, H. Que, and Y . Zhang, “Lightweight prohibited items detection model in x-ray images based on improved yolov7-tiny,”Journal of the Franklin Institute, vol. 362, no. 1, p. 107421, 2025

    2025

  20. [28]

    Fea- ture knowledge distillation-based model lightweight for prohibited item detection in x-ray security inspection images,

    Y . Ren, L. Zhao, Y . Zhang, Y . Liu, J. Yang, H. Zhang, and B. Lei, “Fea- ture knowledge distillation-based model lightweight for prohibited item detection in x-ray security inspection images,” Advanced Engineering Informatics, vol. 65, p. 103125, 2025

    2025

  21. [29]

    Transformer-based dual-view x-ray security inspection image analysis,

    X. Meng, H. Feng, Y . Ren, H. Zhang, W. Zou, and X. Ouyang, “Transformer-based dual-view x-ray security inspection image analysis,” Engineering Applications of Artificial Intelligence , vol. 138, p. 109382, 2024

    2024

  22. [30]

    Eslaxdet: A new x-ray baggage security detection framework based on self-supervised vision transformers,

    J. Wu and X. Xu, “Eslaxdet: A new x-ray baggage security detection framework based on self-supervised vision transformers,” Engineering Applications of Artificial Intelligence , vol. 127, p. 107440, 2024

    2024

  23. [31]

    Self-supervised visual learning in the low-data regime: a comparative evaluation,

    S. Konstantakos, J. Cani, I. Mademlis, D. I. Chalkiadaki, Y . M. Asano, E. Gavves, and G. T. Papadopoulos, “Self-supervised visual learning in the low-data regime: a comparative evaluation,” Neurocomputing, vol. 620, p. 129199, 2025

    2025

  24. [32]

    Tinyvit: Fast pretraining distillation for small vision transformers,

    K. Wu, J. Zhang, H. Peng, M. Liu, B. Xiao, J. Fu, and L. Yuan, “Tinyvit: Fast pretraining distillation for small vision transformers,” in European conference on computer vision , pp. 68–85, Springer, 2022

    2022

  25. [33]

    Detection of novel prohibited item categories for real-world security inspection,

    S. Lin, T. Jia, H. Wang, B. Ma, M. Li, and D. Chen, “Detection of novel prohibited item categories for real-world security inspection,” Engineering Applications of Artificial Intelligence , vol. 144, p. 110110, 2025

    2025

  26. [34]

    Adaptxray: Vision transformer and adapter in x-ray images for prohibited items detection,

    Y . Huang, H. Gao, and X. Li, “Adaptxray: Vision transformer and adapter in x-ray images for prohibited items detection,” in 2024 IEEE International Conference on Image Processing (ICIP) , pp. 402–408, IEEE, 2024

    2024

  27. [35]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll ´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13 , pp. 740–7...

    2014

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