In the scene of monitoring fishing boat targets at the high point of the fishing port, a fishing vessel target detection model based on improved yolov5 is proposed to solve the problems of frequent loss and detection error of fishing vessel detection. Firstly, the anchor frame is re-clustered by the Kmeans++ algorithm, and the anchor frame size suitable for the fishing vessel data set is selected; Then, the weighted bidirectional feature pyramid network (BiFPN) is used to replace the original Feature Pyramid Networks (FPN) + Pixel Aggregation Network (PAN) structure for fast multi-scale feature fusion; Finally, the detection scale of large targets is removed, the detection scale of smaller targets is added, and three new detection scales are used to improve the detection accuracy of the model for small target fishing vessels. The results show that compared with the original yolov5 algorithm, the accuracy, recall and average accuracy of the improved algorithm are improved by 29.5%, 0.5% and 4.5% respectively, and the number of detection frames per second reaches 90.6, which greatly improves the effect of fishing boat target detection.The research shows that the improved yolov5 algorithm meets the accuracy and real-time requirements of fishing vessel target detection during the fishing moratorium.