Enhanced YOLOv8s for lightweight asphalt pavement crack detection under complex road conditions

Abstract

Cracking is a common pavement distress and has a significant impact on structural integrity, traffic safety, and maintenance strategies throughout the entire pavement life cycle. This study presents an improved YOLOv8s-based framework that can effectively and accurately detect asphalt pavement cracks. The proposed model is designed to overcome the main limitations of current methods, such as limited performance, low detection accuracy and limited ability to detect small-scale cracks under complex pavement conditions. To optimize the bounding-box regression and improve the bounding-box localization accuracy of crack instances, a Wise-IoU (WIoU) loss function is introduced. Moreover, Ghost Shuffle Convolution (GSConv) is incorporated into the backbone to improve feature extraction and decrease computational complexity. Furthermore, an improved C2f module is designed to improve the detection of small-scale and low-contrast crack instances. The effectiveness of the proposed method is demonstrated through experimental evaluations on an asphalt pavement crack dataset constructed by combining field-collected images with publicly available pavement crack datasets. Conventional data augmentation operations were further applied to the training images to improve data diversity and model generalization. Experimental results show that the proposed method achieves consistent improvement over the original YOLOv8s model by up to 3.2% points in mean Average Precision (mAP) and 4.0% points in F1-score. In addition, the proposed model performs better in complex pavement environments and can better extract crack-related features. The results indicate that the improved YOLOv8s framework provides an accurate, efficient and intelligent solution for asphalt pavement crack detection, and is a promising method for automated pavement distress evaluation and management.