Pavement Crack Detection and Identification Based on Improved YOLOv8

Abstract

Aiming at the problem of poor real-time performance and low precision of traditional pavement crack detection, an improved YOLOv8 algorithm is proposed to realize the automatic pavement crack detection and identification. Firstly, the crack image is manually labeled by LabelImg labeling software, and then the model parameters are obtained by improving YOLOv8 network. The improved method includes introducing a convolution block attention module (CBAM) to enhance the feature extraction ability, and optimizing the weighted intersection-to-union ratio (Wiou) loss function. Through comprehensive evaluation indexes (F1-measure and mAP@50-95%), the performance of the original model and the improved model in pavement crack detection and recognition is compared. The results show that the improved model has improved the detection precision, F1 value and mAP@50-95% value, which are 2.91%, 3.29% and 1.7% respectively. These results show that the improved model has significant practical significance in realizing the automatic identification of pavement cracks.