In order to reduce the influence of background interference on pavement defect detection and solve the problem that the features that can be extracted from smallsized slender cracks are very limited, this paper is improved based on the YOLOv8 model. Firstly, the C2f-Dysnake module was designed by fusing the C2f in the network with dynamic serpentine convolution, which improved the sensitivity to the shape and boundary of the target and enhanced the feature extraction ability of the slender cracks. Secondly, the reparameterized generalization feature pyramid network RepGFPN and the dynamic upsampler DySample were combined to form a new neck network RDFPN, which increased the attention to the low-level feature map and improved the detection ability of small targets. Finally, the MPCA attention mechanism is added to the backbone network to capture the position relationship at different scales and improve the feature extraction ability of the backbone network. Experimental results show that the improved algorithm improves mAP50 by 2.3% and reaches 98 fps on the RDD2022 dataset, and the detection speed reaches 98 fps, which has obvious advantages over other algorithms and verifies the effectiveness and superiority of the proposed method.