Research on road detection algorithm based on improved YOLOv5s
Huiwen Xue, Yunyun Dong · 2023
This paper proposes a road condition detection approach based on modified YOLOv5s to address the issues of low detection accuracy and high leakage rate in the current road condition detection algorithms. Firstly, the ASPP module is introduced to not only expand the sensory field to obtain rich background information but also retain the key information of small targets in the original feature map, thus improving the detection precision in complex traffic environments and solving the problem of small target miss detection; secondly, through the localization loss function SIoU, a new angle loss is added to the penalty term to further improve the detection accuracy and convergence speed of the model. The experimental results show that the improved model achieves 84.8% detection precision, 89.1% mAP, and a detection speed of 29 frames/s. The improved model can be applied to various complex traffic environments and meet the requirements of road condition detection in the realm of intelligent driving.