This paper proposes an improved TAS-YOLO network model method based on YOLOv5s to address the issues of low accuracy, high missed and false detection rates, and difficulty in collecting uniformly distributed defect types datasets for detecting small road surface defects. Firstly, in the prediction result stage, a context decoupling head for a specific task is used to enhance the accuracy of the localization detection box by separating classification and localization tasks; secondly, by using the FPN structure to input feature maps of 5 scales into the decoupling head for prediction, the multi-scale feature information of small targets is enhanced; finally, use the silde loss function to optimize YOLOv5 and improve the detection accuracy of difficult to classify samples. The experimental results showed that TAS-YOLO algorithm improved the average detection accuracy of various defects, with mAP50 reaching 91.4% and FPS reaching 126, which improved the detection accuracy and efficiency compared with mainstream detection algorithms such as YOLOv7l, YOLOv8s, YOLOv9c-gelan and Efficientdet.