Abstract
The current road damage detection (RDD) algorithms fail to achieve automatic and accurate evaluation and application in the traffic scenarios. In this paper, we propose a RDD algorithm YOLOv7-RDD based on the YOLOv7 model. The data augmentation method CutPaste is introduced for the first time, which can learn the irregularity of damage characteristics, construct pseudo damage samples with high similarity, and create a priori conditions for features extracted. We introduce the CBAM module into the ELAN module to resist the influence of interfering information. And it makes the model focus more on the feature of small objects and reduce the difficulty of the hard objects. In addition, we propose a new dataset RDDBJ, which contains five categories of road damage in 5390 images. And they are high-resolution from a top view, which are more suitable for detection and localization than others. Experiments on the RDDBJ dataset shows that the mAP reaches by 61.9% and is improved by 3.3% compared to the baseline, which is competitive and inspiring.
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Acknowledgments
This study was sponsored by the BUCEA Post Graduate Innovation Project [No. PG2022145].
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Zhang, D., Xu, Z. (2023). Improved YOLOv7 for Road Damage Detection. In: Qian, Z., Jabbar, M., Cheung, S.K.S., Li, X. (eds) Proceeding of 2022 International Conference on Wireless Communications, Networking and Applications (WCNA 2022). WCNA 2022. Lecture Notes in Electrical Engineering, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-99-3951-0_61
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