Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks

To realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contrast, and data augmentation was performed through geometric transformation. Pothole categories were subdivided into P1 and P2 on the basis of whether or not there was water. Then, the Residual Network (ResNet101) and complete IoU (CIoU) loss were used to optimize the structure of the YOLOv3 model, and the K-Means++ algorithm was used to cluster and modify the multiscale anchor sizes. Lastly, the robustness of the proposed model was assessed by generating adversarial examples. Experimental results demonstrated that the proposed model was significantly improved compared with the original YOLOv3 model; the detection mean average precision (mAP) was 89.3%, and the F1-score was 86.5%. On the attacked testing dataset, the overall mAP value reached 81.2% (-8.1%), which shows that this proposed model performed well on samples after random occlusion and adding noise interference, proving good robustness.

Automated summary

In this study, an improved YOLOv3 object detection model was proposed for intelligent and accurate measurement of pavement surface potholes. By combining data augmentation and structure optimization, the proposed model showed significant improvement compared to the original YOLOv3 model and demonstrated good robustness.