DA-YOLOv5: Improved YOLOv5 based on Dual Attention for Object Detection on Coal Chemical Industry

Abstract

The wearing inspection of personnel’s safety protective clothing has important practical significance in the safety production of coal chemical plants. Manual detection or traditional target detection methods are utilized in coal chemical plants for personnel’s safety detection at the moment. However, the clothing detection accuracy is seriously reduced due to the installation position of cameras and the change of light intensity in coal chemical plants. An dual attention based on YOLOv5 is proposed on coal chemical for object detection. Two attention modules, including Efficient Channel Attention (ECA) and Pyramid Split Attention (PSA) module, are integrated into the Spatial Pyramid Pooling (SPP) module and Bottleneck module of this YOLOv5 network. Thus, more global context information is obtained to make up for the lack of global convolution, and the ability to extract features and learn multi-scale information is enhanced. Safety helmet wearing detect data set (SHWD) and self-made data set in our work are utilized to display the improved method’s effectiveness. Compared with the original YOLOv5 algorithm, the improved method achieved an average accuracy increase of 2.7% at different thresholds. Numerous comparative experiments further verify the feasibility of the improved method.