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A Comparative Study of YOLO V4 and V5 Architectures on Pavement Cracks Using Region-Based Detection

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Complex Computational Ecosystems (CCE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13927))

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Abstract

The frequent utilization of land transportation systems has led to the further deterioration of roads and caused traffic hazards. Early detection of asphalt pavement distresses has a necessary role in eliminating these hazards. Implementing an efficient automated method for detecting, locating, and classifying pavement distresses could help to address this problem in its early phase. This automated system has the potential to assist governments in maintaining road conditions effectively, especially those that aim to build smart cities. Furthermore, smart cars equipped with sensors and cameras can further contribute to road conditions and pavement distress inspection. The YOLO algorithm has demonstrated its potential to automate the detection process with real-time object detection and has shown promising results to be integrated into smart cars. The primary focus of this paper was to compare the performance of YOLOv4 and YOLOv5 in detecting thin and small crack objects using two publicly available image datasets, EdmCrack600 and RDD2022. Our comparisons were based not only on the architectures themselves but also on the number of classes in datasets that represent various types of pavement cracks. Additionally, we introduced an augmentation technique that is specific to crack objects in order to address the imbalanced class representation in the EdmCrack600 dataset. This technique improved final results by 11.2%. Overall, our comparisons indicated that YOLOv5 demonstrated better accuracy by achieving a mean average precision (mAP) of 65.6% on the RDD2022 dataset, and a mAP of 42.3% on the EdmCrack600 dataset.

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Notes

  1. 1.

    https://github.com/kiyoshiiriemon/yolov4_darknet#how-to-train-to-detect-your-custom-objects.

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Author information

Authors and Affiliations

  1. ICUBE Laboratory, UMR CNRS 7357, University of Strasbourg, Strasbourg, France

    Rauf Fatali, Samer El Zant & Rabih Amhaz

  2. French-Azerbaijani University (UFAZ), Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rauf Fatali, Gulzar Safarli & Samer El Zant

  3. Icam, site de Strasbourg-Europe, Strasbourg, France

    Rauf Fatali & Rabih Amhaz

Authors
  1. Rauf Fatali
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  2. Gulzar Safarli
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  3. Samer El Zant
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  4. Rabih Amhaz
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Corresponding author

Correspondence to Rauf Fatali .

Editor information

Editors and Affiliations

  1. Strasbourg University, Strasbourg, France

    Pierre Collet

  2. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Latafat Gardashova

  3. Strasbourg University, Strasbourg, France

    Samer El Zant

  4. French Azerbaijani University, Baku, Azerbaijan

    Ulviya Abdulkarimova

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Fatali, R., Safarli, G., El Zant, S., Amhaz, R. (2023). A Comparative Study of YOLO V4 and V5 Architectures on Pavement Cracks Using Region-Based Detection. In: Collet, P., Gardashova, L., El Zant, S., Abdulkarimova, U. (eds) Complex Computational Ecosystems. CCE 2023. Lecture Notes in Computer Science, vol 13927. Springer, Cham. https://doi.org/10.1007/978-3-031-44355-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-44355-8_4

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  • Online ISBN: 978-3-031-44355-8

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