QL-YOLOv8s: Precisely Optimized Lightweight YOLOv8 Pavement Disease Detection Model

Detecting road surface defects is essential for highway maintenance, yet the application of most models is hindered by the limitations of existing detection resources. To address this challenge, we have enhanced YOLOv8, introducing a lightweight detection model dubbed QL-YOLOv8s. In this study, we employ the DIoU loss function to optimize bounding box regression, taking into account both the size of overlapping areas and the distance between the centers of boxes, thereby handling targets of various sizes and shapes with improved localization accuracy. Moreover, a lightweight Mixed Local Channel Attention (MLCA) has been incorporated into the backbone of the model, aimed at enhancing the recognition capabilities in complex environments without in-creasing the model's burden. Furthermore, by integrating the Dilated Wrapping Residual (DWR) module and C2f into BiFPN, we developed a new neck structure, BiFPN-D, and introduced a lightweight detection head, Detect-T3, thus augmenting the model's feature perception capacity, reducing parameter count, and boosting detection speed. Based on the RDD 2022 public dataset, QL-YOLOv8s demonstrated a reduction in parameter count and size by 37%, a decrease in com-putational requirements by 19%, and achieved an average precision of mAP0.5 at 95.8%. These results underscore the contribution and practical value of our method to the technology of automatic road defect detection.