YOLOv8-DATB: a lightweight road defect detection model

To address the issues of low accuracy and high computational demand in road defect detection, This study puts forward a lightweight detection network, YOLOv8-DATB, built upon an enhanced YOLOv8n. Initially, the C2f module in the backbone is substituted with the C2f-DAT to effectively extract key regional features in road defect images while significantly reducing computational load. To bolster the model's capacity for handling features across various scales and enhance its fusion efficacy, a modified, lightweight Bi-directional Feature Pyramid Network (BiFPN) is integrated into the network's neck component. This strategic inclusion aims at not only optimizing the model's ability to detect smaller objects with heightened precision but also curbing the increase in model parameters, thereby achieving a balance between efficiency and performance. Compared to the original algorithm, the YOLOv8-DATB improves accuracy and mean average precision (mAP) by 4.9% and 1.1%, respectively, while reducing parameters by 33.7% and computational load by 11.1%. Additionally, it shows a significant increase in detection accuracy compared to other mainstream models.