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Smart city article details
| Title | A Comparative Study Of Yolo V4 And V5 Architectures On Pavement Cracks Using Region-Based Detection |
|---|---|
| ID_Doc | 816 |
| Authors | Fatali R.; Safarli G.; El Zant S.; Amhaz R. |
| Year | 2023 |
| Published | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13927 LNCS |
| DOI | http://dx.doi.org/10.1007/978-3-031-44355-8_4 |
| 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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Architecture comparison; Computer Vision; Crack Detection; Deep learning; Pattern Recognition; Smart cars; Smart cities; YOLO |
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