Pith. sign in

REVIEW 4 major objections 4 minor 57 references

Deep learning framework for crater detection and identification on the Moon and Mars

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A two-stage deep-learning framework detects and identifies impact craters on the Moon and Mars, with YOLO the most balanced and ResNet-50 the most precise on large craters.

desk verdict Plausible but unverifiable benchmark comparison of standard crater-detection models; the full text is garbled and the evaluation dependencies are undocumented. read the letter →

arxiv 2508.03920 v1 pith:PHTQIPX3 submitted 2025-08-05 cs.CV cs.AI

Yihan Ma , Zeyang Yu , Rohitash Chandra This is my paper · ORCID
classification cs.CVcs.AI
keywords craterdetectiondeeplearningYOLOResNet-50CNNMoonMarsremotesensing
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 tries to establish a practical division of labor for automated crater mapping on the Moon and Mars: YOLO gives the most balanced trade-off between finding craters and avoiding false alarms, while ResNet-50 is the most reliable when the goal is to identify large craters with high precision. The authors build a two-stage framework in which the first stage identifies craters using a classic CNN, ResNet-50, or YOLO, and the second stage uses YOLO-based detection to localize them. They evaluate on selected regions of Mars and the Moon using remote-sensing data and report per-region results. If the comparison holds, planetary researchers get a concrete model-selection rule: use YOLO for general crater surveys and ResNet-50 for large-crater cataloging.

What carries the argument

The central mechanism is the two-stage pipeline: Stage 1 classifies image patches as containing craters using a classic CNN, ResNet-50, or YOLO; Stage 2 uses YOLO-based object detection to draw bounding boxes around identified craters. The argument turns on comparing YOLO and ResNet-50 within the same framework, so the reported differences in precision and recall are attributed to the identification model rather than to different localization machinery.

What would settle it

Take a set of regions where an independent, more complete crater catalog exists, run the same two-stage pipeline, and compare results against both the original and the independent labels. If YOLO's balanced precision-recall and ResNet-50's large-crater edge shrink or reverse when the reference labels change, the central comparison is an artifact of the original catalog.

Watch

Extended reading notes

Core claim

The paper's central claim is that a two-stage deep-learning framework can detect and identify impact craters on selected regions of the Moon and Mars with a useful model-dependent trade-off: YOLO achieves the most balanced precision-recall performance across craters, while ResNet-50 achieves the highest precision on large craters. The first stage performs crater identification using a classic CNN, ResNet-50, and YOLO; the second stage uses YOLO-based detection to localize the craters identified. The authors evaluate on remote-sensing data for selected regions and report per-region results, including a summary that connects detected craters to remote-sensing context.

Load-bearing premise

The evaluation assumes the ground-truth crater catalog for the selected Moon and Mars regions is complete and correct; if it misses many small or degraded craters, the reported precision-recall balance and the large-crater advantage reflect label gaps rather than genuine detection skill.

Editorial extensions

If this is right

  • Planetary researchers can choose YOLO when a balanced crater survey is needed and ResNet-50 when large craters must be captured with few false positives.
  • The two-stage design separates identification from localization, so either stage can be improved or replaced independently.
  • The per-region summary format can be reproduced for other selected areas on the Moon and Mars, making the framework a reusable mapping tool.
  • Automated screening of this kind can reduce the manual effort of crater counting in routine planetary mapping work.

Reading between the lines

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

  • Because the paper does not document how the selected regions were chosen, I would not assume the same balance holds on all terrains; a direct extension is to test the same pipeline across highlands, maria, polar terrain, and varied lighting.
  • If the ResNet-50 large-crater advantage generalizes, it has a natural downstream use in crater size-frequency dating, where large craters dominate age estimates; the paper itself stops at detection and identification.
  • The reliance on existing crater catalogs suggests the safest practical use is candidate generation for human verification rather than a final ground-truth catalog.
  • The same two-stage structure could be pointed at other airless bodies such as Mercury or Ceres, though crater morphology differs; a testable extension is to check whether the same balance-versus-precision split appears there.
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

4 major / 4 minor

Summary. The manuscript proposes a two-stage deep-learning framework for crater detection and identification on the Moon and Mars. Stage one applies three classifiers—described as a simple classic CNN, ResNet-50, and YOLO—to identify craters, and stage two uses YOLO for localization. The authors report per-class precision/recall/F1/support results and detection tables for selected regions, concluding that YOLO gives the most balanced crater detection performance while ResNet-50 excels at large craters with high precision. The central claim is empirical and benchmark-oriented. Unfortunately, the supplied full text is largely unreadable because of character-encoding corruption (mojibake), so most methodological details—dataset splits, ground-truth catalog, thresholds, training setup, and region selection—cannot be verified from the manuscript as provided.

Significance. If the empirical comparison is correctly executed, the paper would provide a useful applied benchmark comparing standard deep-learning detectors for planetary crater mapping. The claimed trade-off—YOLO balancing precision and recall while ResNet-50 favors large-crater precision—is clear, internally consistent, and in principle falsifiable from the reported tables. The contribution is modest rather than architecturally novel, since YOLO and ResNet are established models. No code, data, or machine-checked artifacts are supplied in the visible text, so the value rests entirely on the credibility and completeness of the evaluation protocol, which is currently not documented in a verifiable way.

major comments (4)
  1. [Entire manuscript (as supplied)] The body of the manuscript is corrupted by character-encoding errors (mojibake). I cannot read the methods, dataset description, evaluation protocol, or discussion. Only the abstract and fragmentary tables are legible. This prevents verification of every load-bearing claim in the paper. A clean, legible manuscript is a prerequisite for any further technical review; the current file cannot be evaluated as a scientific article.
  2. [Per-class precision/recall/F1 tables (tables with headers 'precision recall f1-score support')] These tables are the evidence for the abstract's central trade-off, but the manuscript does not document the train/test split, the source and version of the ground-truth crater catalog, or whether stage-1 classifiers and the stage-2 YOLO detector were trained on labels from the same catalog. If small craters are underlabeled in the catalog, YOLO's false positives can include real but unlabeled craters, while ResNet-50's large-crater precision can simply reflect denser labeling of large craters. This is a concrete leakage path. Please report held-out geographic regions, catalog provenance, and a completeness analysis by crater size.
  3. [Detection results tables (Section 4, 'Crater detection and identification')] The detection evaluation lacks a stated protocol. The manuscript does not specify the IoU matching threshold, the confidence threshold for YOLO, non-maximum-suppression settings, or the rule for counting a predicted box as a true positive. All precision and recall values are threshold-dependent, so the claim that YOLO is 'most balanced' and ResNet-50 has 'high precision on large craters' is not identified under a fixed, reproducible criterion. Please report the full protocol or provide precision-recall curves.
  4. ['Summary report with remote sensing data' (Section 5)] The selected regions on the Moon and Mars are not specified with coordinates, image sources, resolutions, or lighting conditions. Without a documented selection protocol, the reported numbers cannot be reproduced and cannot be interpreted outside the specific chosen tiles. The paper should give region identifiers and a rationale for why these regions are representative.
minor comments (4)
  1. [Abstract] The abstract describes YOLO and ResNet as 'novel models,' which is misleading since they are established architectures. Suggest using 'modern deep-learning models' or similar.
  2. [Introduction / Section 1] The phrase 'different types of craters' is used without defining the type taxonomy. Please specify the classes used in the per-class tables.
  3. [Figures] Figure captions and axis labels are not readable in the supplied text. In the resubmission, ensure all figures are legible and include scale bars and coordinate information where relevant.
  4. [References] The reference list is garbled and incomplete in the provided file. A complete, correctly formatted bibliography is required for review.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical benchmark; detection metrics are measurements from test data, not re-statements of the training labels or of the model definitions.

full rationale

I walked the paper's claimed derivation chain. The central claim — YOLO gives the most balanced crater detection while ResNet-50 excels at large craters with high precision — is an experimental comparison, not a derivation from first principles. The reported precision/recall/F1 tables are computed from model outputs against the crater catalog; unless the test population were the training population (no evidence in the legible text), the metrics are not circular with respect to the models' equations. The two-stage architecture (stage-1 identification by CNN/ResNet/YOLO, stage-2 YOLO localization) reuses models but does not define any output in terms of another output. No self-citation or uniqueness theorem is invoked to forbid alternatives. The manuscript's own Limitations section (end of paper) concedes that performance is constrained by the training data and may not generalize; that is a data-quality/generalizability caveat, not an admission that the reported numbers are constructed from the definitions. The skeptic's concern about incomplete crater catalogs and possible train/test overlap is a correctness and evaluation-validity risk, not a circularity: an incomplete label set can bias precision/recall, but the metric would still be an empirical measurement of the model against that label set. Accordingly, no circular step can be quoted, and the score is 0.

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

The central claim rests on three unpaid premises: the correctness of the crater label catalog used for training and evaluation, the adequacy of the input imagery, and the reliability of the standard architectures it borrows. The free parameters are the usual deep learning controls (thresholds, training hyperparameters) plus the choice of evaluation regions. These are typical for a benchmark paper, but their values are not stated in any legible part of the manuscript, which caps the reproducibility and soundness scores.

free parameters (3)
  • Detection thresholds (confidence and IoU) for YOLO and evaluation
    Precision/recall trade-offs are threshold-dependent; the abstract reports trade-offs without giving thresholds, and these are chosen values, not derived.
  • Training hyperparameters (learning rate, epochs, tile size, batch size)
    Standard for deep learning benchmarks; these control the reported accuracies and are empirically chosen.
  • Choice of 'selected regions' on Moon and Mars
    The evaluation and the summary report are tied to regions chosen by the authors; performance is conditional on this choice.
assumptions (3)
  • domain assumption Ground-truth crater labels for the selected regions are complete and correct
    Precision and recall are measured against this catalog; incomplete labels bias the reported metrics. Entered at the dataset construction step.
  • domain assumption Input remote sensing imagery has sufficient resolution and quality to resolve the craters being detected
    Sub-resolution or low-contrast craters are undetectable by any model; the framework's ceiling is set by the imagery.
  • standard math Standard CNN, ResNet-50, and YOLO architectures and training recipes behave as per their source publications
    The paper relies on pre-existing architectures, pretrained weights where used, and standard optimization; it does not re-derive them.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep learning framework for crater detection and identification on the Moon and Mars." pith.science (2026). https://pith.science/paper/PHTQIPX3

@misc{pith2026250803920,
  author       = {Pith},
  title        = {Pith review of: Deep learning framework for crater detection and identification on the Moon and Mars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PHTQIPX3}},
  note         = {Machine review of arXiv:2508.03920}
}
read the original abstract

Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Their spatial distribution and morphological characteristics provide critical information on planetary surface composition, geological history, and impact processes. In recent years, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advancements in deep learning models for impact crater detection and identification. We use novel models, including Convolutional Neural Networks (CNNs) and variants such as YOLO and ResNet. We present a framework that features a two-stage approach where the first stage features crater identification using simple classic CNN, ResNet-50 and YOLO. In the second stage, our framework employs YOLO-based detection for crater localisation. Therefore, we detect and identify different types of craters and present a summary report with remote sensing data for a selected region. We consider selected regions for craters and identification from Mars and the Moon based on remote sensing data. Our results indicate that YOLO demonstrates the most balanced crater detection performance, while ResNet-50 excels in identifying large craters with high precision.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 36 canonical work pages

  1. [1]

    Shirmard, E

    H. Shirmard, E. Farahbakhsh, E. Heidari, A. B. Pour, B. Pradhan, D. Müller, and R. Chandra, ``A comparative study of convolutional neural networks and conventional machine learning models for lithological mapping using remote sensing data,'' Remote Sensing, vol. 14, p. 819, 2022. [Online]. Available: https://doi.org/10.3390/rs14040819

    work page doi:10.3390/rs14040819 2022

  2. [2]

    Erdem, S

    T. Erdem, S. Stefano, and G. Eberhard, ``Autonomous navigation for deep space small satellites scientific and technological advances,'' Acta Astronautica, vol. 193, 2022. [Online]. Available: https://doi.org/10.1016/j.actaastro.2021.12.030

    doi:10.1016/j.actaastro.2021.12.030 2022

  3. [3]

    C. Yang, H. Zhao, L. Bruzzone, J. A. Benediktsson, Y. Liang, B. Liu, X. Zeng, R. Guan, C. Li, and Z. Ouyang, ``Lunar impact crater identification and age estimation with chang’e data by deep and transfer learning,'' Nature Communications, vol. 11, p. Article 6358, 2020. [Online]. Available: https://doi.org/10.1038/s41467-020-20215-y

    work page doi:10.1038/s41467-020-20215-y 2020

  4. [4]

    K. Di, W. Li, Z. Yue, Y. Sun, and Y. Liu, ``A machine learning approach to crater detection from topographic data,'' Advances in Space Research, vol. 54, pp. 2419--2429, 2014. [Online]. Available: https://doi.org/10.1016/j.asr.2014.08.018

    work page doi:10.1016/j.asr.2014.08.018 2014

  5. [5]

    Xiong, Y

    L. Xiong, Y. Wang, H. Cao, Y. Ren, S. Li, Y. Chen, and G. Tang, ``Deep learning detects entire multiple-size lunar craters driven by elevation data and topographic knowledge,'' Geo-spatial Information Science, 2025. [Online]. Available: https://doi.org/10.1080/10095020.2025.2452932

    arXiv 2025

  6. [6]

    LeCun, Y

    Y. LeCun, Y. Bengio, and G. Hinton, ``Deep learning,'' Nature, vol. 521, pp. 436--444, 2015. [Online]. Available: https://doi.org/10.1038/nature14539

    doi:10.1038/nature14539 2015

  7. [7]

    Deep Learning based Systems for Crater Detection: A Review

    A. Tewari, K. Prateek, A. Singh, and N. Khanna, ``Deep learning based systems for crater detection: A review,'' arXiv preprint arXiv:2310.07727, 2023, computer Vision and Pattern Recognition. [Online]. Available: https://doi.org/10.48550/arXiv.2310.07727

    work page Pith review arXiv doi:10.48550/arxiv.2310.07727 2023

  8. [8]

    Silburt, M

    A. Silburt, M. Ali-Dib, C. Zhu, A. Jackson, D. Valencia, Y. Kissin, D. Tamayo, and K. Menou, ``Lunar crater identification via deep learning,'' Icarus, vol. 317, pp. 27--38, 2018

    work page 2018

Show all 57 references
  1. [9]

    Yang and Z

    S. Yang and Z. Cai, ``High-resolution feature pyramid network for automatic crater detection on mars,'' IEEE Transactions on Geoscience and Remote Sensing, vol. 60, p. 4601012, 2021. [Online]. Available: https://doi.org/10.1109/TGRS.2021.3104925

    2021

  2. [10]

    Y. Li, H. Zhang, X. Xue, Y. Jiang, and Q. Shen, ``Deep learning for remote sensing image classification: A survey,'' Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 6, p. e1264, 2018

    2018

  3. [11]

    S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. A. Benediktsson, ``Deep learning for hyperspectral image classification: An overview,'' IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690--6709, 2019

    2019

  4. [12]

    LeCun, L

    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, ``Gradient-based learning applied to document recognition,'' Proceedings of the IEEE, vol. 86, no. 11, pp. 2278--2324, 1998. [Online]. Available: https://doi.org/10.1109/5.726791

    1998 doi

  5. [13]

    Ronneberger, P

    O. Ronneberger, P. Fischer, and T. Brox, ``U-net: Convolutional networks for biomedical image segmentation,'' in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 234--241. [Online]. Available: https://doi.org/10.1007/978-3-319-24574-4_28

    2015 doi

  6. [14]

    S. Ren, K. He, R. Girshick, and J. Sun, ``Faster r-cnn: Towards real-time object detection with region proposal networks,'' Advances in Neural Information Processing Systems, vol. 28, pp. 91--99, 2015. [Online]. Available: https://doi.org/10.1109/TPAMI.2016.2577031

    2015

  7. [15]

    L. Cui, K. Liu, and N. Chen, ``A review on degradation modeling in reliability analysis,'' IEEE Transactions on Reliability, vol. 69, no. 4, pp. 1169--1189, 2020. [Online]. Available: https://doi.org/10.1109/TR.2020.2973618

    2020

  8. [16]

    Redmon, S

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, ``You only look once: Unified, real-time object detection,'' in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1em plus 0.5em minus 0.4em IEEE, 2016, pp. 779--788

    2016

  9. [17]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770--778. [Online]. Available: https://doi.org/10.1109/CVPR.2016.90

    2016 doi

  10. [18]

    L. K. Ramasamy, J. Kakarla, B. V. Isunuri, and M. Singh, ``Multi-class brain tumor classification using residual network and global average pooling,'' Multimedia Tools and Applications, vol. 80, pp. 13\,429--13\,438, 2021

    2021

  11. [19]

    X. Chen, X. Wang, K. Zhang, Y. Guo, and J. Sun, ``Recent advances and clinical applications of deep learning in medical image analysis,'' arXiv preprint arXiv:2105.13381, 2021, medical diagnosis and image analysis. [Online]. Available: https://arxiv.org/abs/2105.13381

    2021 arXiv

  12. [20]

    S. K. Sahoo, S. Kumar, M. Z. H. Abedin, M. K. Tiwari, and A. Gunasekaran, ``Deep learning applications in manufacturing operations: a review of trends and ways forward,'' Journal of Enterprise Information Management, vol. 35, no. 5, pp. 1215--1240, 2022

    2022

  13. [21]

    Tewari, K

    A. Tewari, K. Prateek, A. Singh, and N. Khanna, ``Deep learning based systems for crater detection: A review,'' arXiv preprint arXiv:2310.07727, 2023

    2023 arXiv

  14. [22]

    Del Prete, A

    R. Del Prete, A. Saveriano, and A. Renga, ``A deep learning-based crater detector for autonomous vision-based spacecraft navigation,'' in 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace). 1em plus 0.5em minus 0.4em IEEE, 2022, pp. 231--236

    2022

  15. [23]

    Y. Zhao, X. Zhang, W. Feng, and J. Xu, ``Deep learning classification by resnet-18 based on the real spectral dataset from multispectral remote sensing images,'' Remote Sensing, vol. 14, p. 4883, 2022

    2022

  16. [24]

    Li, ``Robustness analysis of traffic sign recognization based on resnet,'' Highlights in Science, Engineering and Technology, 2023

    K. Li, ``Robustness analysis of traffic sign recognization based on resnet,'' Highlights in Science, Engineering and Technology, 2023

    2023

  17. [25]

    X. Zhao, W. Liu, W. Xing, and X. Wei, ``Da-res2net: a novel densely connected residual attention network for image semantic segmentation,'' KSII Transactions on Internet and Information Systems, vol. 14, pp. 4426--4442, 2020

    2020

  18. [26]

    S. J. Pan and Q. Yang, ``A survey on transfer learning,'' IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345--1359, 2010. [Online]. Available: https://doi.org/10.1109/TKDE.2009.191

    2010 doi

  19. [27]

    K. Di, W. Li, Z. Yue, Y. Sun, and Y. Liu, ``A machine learning approach to crater detection from topographic data,'' Advances in Space Research, vol. 54, pp. 2419--2429, 2014

    2014

  20. [28]

    E. R. Urbach and T. F. Stepinski, ``Automatic detection of sub-km craters in high resolution planetary images,'' Planetary and Space Science, vol. 57, no. 7, pp. 880--887, 2009. [Online]. Available: https://doi.org/10.1016/j.pss.2009.03.009

    2009 doi

  21. [29]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' arXiv preprint arXiv:1512.03385, 2015. [Online]. Available: https://doi.org/10.48550/arXiv.1512.03385

    2015 doi arXiv

  22. [30]

    Krizhevsky, I

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, ``Imagenet classification with deep convolutional neural networks,'' Communications of the ACM, vol. 25, pp. 1097--1105, 2012. [Online]. Available: https://doi.org/10.1145/306538

    2012 doi

  23. [31]

    B. Wu, H. Ye, W. Huang, H. Wang, P. Luo, Y. Ren, and W. Kong, ``Monitoring the vertical distribution of maize canopy chlorophyll content based on multi-angular spectral data,'' Remote Sensing, vol. 13, no. 5, p. 987, 2021. [Online]. Available: https://doi.org/10.3390/rs13050987

    2021 doi

  24. [32]

    Redmon and A

    J. Redmon and A. Farhadi, ``Yolov3: An incremental improvement,'' arXiv preprint arXiv:1804.02767, 2018. [Online]. Available: https://arxiv.org/abs/1804.02767

    2018 arXiv

  25. [33]

    [Online]

    ------, ``Yolo9000: Better, faster, stronger,'' arXiv preprint arXiv:1612.08242, 2016. [Online]. Available: https://doi.org/10.48550/arXiv.1612.08242

    2016 doi arXiv

  26. [34]

    A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, ``Mobilenets: Efficient convolutional neural networks for mobile vision applications,'' arXiv preprint arXiv:1704.04861, 2017. [Online]. Available: https://doi.org/10.48550/arXiv.1704.04861

    2017 doi arXiv

  27. [36]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Identity mappings in deep residual networks,'' arXiv preprint arXiv:1603.05027, 2016. [Online]. Available: https://doi.org/10.48550/arXiv.1708.02002

    2016 doi arXiv

  28. [37]

    Huang, Z

    G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, ``Densely connected convolutional networks,'' arXiv preprint arXiv:1608.06993, 2016. [Online]. Available: https://doi.org/10.48550/arXiv.1608.06993

    2016 doi arXiv

  29. [38]

    Yosinski, J

    J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, ``How transferable are features in deep neural networks?'' arXiv preprint arXiv:1411.1792, 2014. [Online]. Available: https://doi.org/10.48550/arXiv.1411.1792

    2014 doi arXiv

  30. [39]

    A. S. McEwen, E. M. Eliason, J. W. Bergstrom, N. T. Bridges, C. J. Hansen, W. A. Delamere, J. A. Grant, V. C. Gulick, and K. E. Herkenhoff, ``Mars reconnaissance orbiter's high resolution imaging science experiment (hirise),'' Journal of Geophysical Research: Planets, vol. 112...

    2007 doi

  31. [40]

    Hamelin, C

    M. Hamelin, C. Béghin, R. Grard, J. López-Moreno, K. Schwingenschuh, F. Simões, R. Trautner, J. Berthelier, V. Brown, M. Chabassière, P. Falkner, F. Ferri, M. Fulchignoni, I. Jernej, J. Jeronimo, G. Molina-Cuberos, R. Rodrigo, and T. Tokano, ``Electron conductivity and density...

    1964

  32. [41]

    Ciaglia, F

    F. Ciaglia, F. S. Zuppichini, P. Guerrie, M. McQuade, and J. Solawetz, ``Roboflow 100: A rich, multi-domain object detection benchmark,'' arXiv preprint arXiv:2211.13523, 2022

    2022 arXiv

  33. [42]

    [Online]

    NASA Planetary Data System , ``Mars reconnaissance orbiter hirise data,'' NASA Planetary Data System. [Online]. Available: https://pds-imaging.jpl.nasa.gov/search/?fq=MRO_IMAGE_CLASS

  34. [43]

    Neubeck and L

    A. Neubeck and L. Van Gool, ``Efficient non-maximum suppression,'' in 18th International Conference on Pattern Recognition (ICPR'06), vol. 3. 1em plus 0.5em minus 0.4em IEEE, 2006, pp. 850--855. [Online]. Available: https://doi.org/10.1109/ICPR.2006.479

    2006 doi

  35. [44]

    [Online]

    NASA , ``Nasa mars exploration program,'' 2024, accessed: 2025-04-01. [Online]. Available: https://mars.nasa.gov

    2024

  36. [45]

    [Online]

    Roboflow, ``Roboflow universe: Public datasets for computer vision,'' 2024, accessed: 2025-04-01. [Online]. Available: https://universe.roboflow.com

    2024

  37. [46]

    Goodfellow, Y

    I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. 1em plus 0.5em minus 0.4em MIT Press, 2016. [Online]. Available: https://www.deeplearningbook.org/

    2016

  38. [47]

    C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, ``A survey on deep transfer learning,'' International Conference on Artificial Neural Networks, pp. 270--279, 2018

    2018

  39. [48]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., ``Pytorch: An imperative style, high-performance deep learning library,'' in Advances in Neural Information Processing Systems, vol. 32. 1em plus 0.5em minus 0...

    2019

  40. [49]

    Chollet, ``Keras,'' https://github.com/keras-team/keras, 2015

    F. Chollet, ``Keras,'' https://github.com/keras-team/keras, 2015

    2015

  41. [50]

    D. P. Kingma and J. Ba, ``Adam: A method for stochastic optimization,'' arXiv preprint arXiv:1412.6980, 2014. [Online]. Available: https://doi.org/10.48550/arXiv.1412.6980

    2014 doi arXiv

  42. [51]

    Zhao and L

    Y. Zhao and L. Liu, ``Object detection using sliding window on large images: A case study on remote sensing imagery,'' Remote Sensing, vol. 12, no. 5, p. 845, 2020

    2020

  43. [52]

    Ultralytics, ``Yolo by ultralytics,'' https://github.com/ultralytics/ultralytics, 2023, available at https://github.com/ultralytics/ultralytics

    2023

  44. [53]

    M. A. et al., ``Tensorflow: Large-scale machine learning on heterogeneous systems,'' 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org

    2015

  45. [54]

    C. R. Harris, K. J. Millman, S. J. van der Walt et al., ``Array programming with numpy,'' Nature, vol. 585, pp. 357--362, 2020

    2020

  46. [55]

    Bradski, ``The opencv library,'' Dr

    G. Bradski, ``The opencv library,'' Dr. Dobb's Journal of Software Tools, 2000

    2000

  47. [56]

    J. D. Hunter, ``Matplotlib: A 2d graphics environment,'' pp. 90--95, 2007

    2007

  48. [57]

    Chollet, Deep Learning with Python

    F. Chollet, Deep Learning with Python. 1em plus 0.5em minus 0.4em Manning Publications, 2017

    2017

  49. [58]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.