Intelligent Detection of Road Cracks Based on Improved YOLOv5
DOI:
https://doi.org/10.14464/ess.v10i7.599Abstract
With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and mAP@0.5 compared with YOLOv5s, among which mAP@0.5 has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects.

Downloads
Published
Issue
Section
License
Copyright (c) 2023 Zhiyan Zhou, Xiaoyu Yu, Yuji Iwahori, Qing Wu, Haibin Wu, Aili Wang

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright for articles published in this journal is retained by the authors. The content is published under a Creative Commons Licence Attribution 4.0 International (CC BY 4.0). This permits use, distribution, and reproduction in any medium, provided the original work is properly cited, and is otherwise in compliance with the licence.