Reflective Safety Clothes Wearing Detection in Hydraulic Engineering Using YOLOv3-CCD

Abstract

The construction site of the hydraulic engineering has a high danger factor and the correct wearing of reflective safety clothes ensures the safety of workers. Therefore, the detection and testing of reflective safety clothes wearing is an important task in the construction site of a hydraulic engineering. However, the traditional manual supervision strategy has the problems of low efficiency, narrow scope, and poor real-time performance in complex working conditions. Based on YOLOv3 that is a classical target detection model, this paper proposes a reflective safety clothes detection algorithm (YOLOv3-CCD) based on an attention mechanism and an improved loss function. By adding the CA (Coordinate Attention) mechanism module to the backbone network, the characterization ability of the target feature is enhanced, so as to solve the Long-Term dependencies problem in the detection process; The loss function is changed from IOU-Loss (Intersection Over Union Loss) to CIOU-Loss (Complete-IOU Loss), so that the network takes the aspect ratio into consideration when selecting the prediction box, which improves the accuracy of target positioning; In the post-processing of the algorithm, we improved NMS (Non-Maximum Suppression) to solve the problem of dense target detection being missed. Experimental results show that compared with the original YOLOv3 network model, the algorithm has stronger robustness and the overall detection accuracy is 1.8% higher than that of the original network. Moreover, the detection speed is 32 frames per second, which is faster than the original network.