SDFC-YOLO: A YOLO-Based Model With Selective Dynamic Feature Compensation for Pavement Distress Detection

Timely detection and treatment of road cracks are crucial to prevent further deterioration of pavement. An accurate road crack detection algorithm can significantly reduce the human resources required for pavement maintenance. However, existing CNN-based object detectors, such as the You Only Look Once (YOLO) series of algorithms, face challenges such as receptive field fixation and information loss during feature extraction, resulting in lower accuracy in road crack detection. Therefore, we propose the Selective Dynamic Feature Compensation-YOLO (SDFC-YOLO) algorithm for pavement distress detection. Firstly, we introduce the Dynamic Downsampling Module (DDM), which adaptively adjusts the sampling positions of the convolutional kernel during the feature extraction process, addressing the issue of a fixed receptive field. Secondly, we propose a novel feature fusion method compensating for lost feature information in the path aggregation network. Lastly, we design a multi-scale weight selection module based on the above feature fusion method. It aims to utilize the channel weights of high-level features to guide bottom-level features and select more important features for compensation, thereby further enhancing detection accuracy. Experimental results demonstrate that compared to the benchmark model YOLOv8s, our method improves Precision (P), Recall (R), mean Average Precision@0.5 (mAP@0.5), and F1 score on the UAPD dataset by 6.8%, 2.4%, 5.3%, and 4.3%, respectively. Similarly, on the UAV-PDD2023 dataset, the aforementioned metrics are enhanced by 3.1%, 4.4%, 2.7%, and 3.8%, respectively. Furthermore, our method takes only 10.53ms to process a $1280\times 1280$ resolution image, which fully meets the requirement of real-time detection.