Abstract: This paper proposes a reflective vest detection algorithm for apron staff based on prior knowledge and improved YOLOv3 algorithm.Aiming at the problem of the existing target detection method with low speed, the reflective vest detection candidate region is generated based on prior knowledge to replace the initial candidate region, so as to reduce the detection area.Darknet-37 is used to replace Darknet-53 as the backbone network for feature extraction, which improves the detection speed of the algorithm.Aiming at the problem that the reflective vest occupies a small area in the picture and is difficult to identify, a spatial pyramid pooling structure (SPP) is added into the detection model to realize feature enhancement, and the detection scale is increased to four for multi-scale feature fusion.The K-means++algorithm is used to perform cluster analysis again on the size of labeled bounding box, and the clustering result is used to replace the initial Anchor value of Yolov3.GIoU is selected as the loss function to improve the positioning accuracy.Experimental results show that the proposed new target detection algorithm in the self-built reflective vest data set is better than YOLOv3 test results, the precision rate and recall rate reach 97.6% and 96.1%, detection rate reach 28.4 frames/s, which effectively solves the problems such as inaccurate positioning, missed detection and low detection speed existing in the original model, and meets the real-time requirements in the practical application of apron target detection while ensuring a high detection accuracy.

Key words: Feature fusion, Object detection, Real-time detection, Reflective vest detection, Spatial pyramid pooling