Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4

Abstract

The accurate and timely detection of irregular behavior of substation personnel plays an important role in maintaining personal safety and preventing power outage accidents. This paper proposes a method for irregular behaviors detection (IBD) of substation personnel based on an improved YOLOv4 which uses MobileNetV3 to replace the CSPDarkNet53 feature extraction network, depthwise separable convolution and efficient channel attention (ECA) to optimize the SPP and PANet networks, and four scales of feature maps to fuse to improve the detection accuracy. First, an image dataset was constructed using video data and still photographs preprocessed by the gamma correction method. Then, the improved YOLOv4 model was trained by combining Mosaic data enhancement, cosine annealing, and label smoothing skills. Several detection cases were carried out, and the experimental results showed that the proposed improved YOLOv4 model has high accuracy, with a mean average precision (mAP) of 83.51%, as well as a fast detection speed, with a frames per second (FPS) of 38.06 pictures/s. This represents better performance than other object detection methods, including Faster RCNN, SSD, YOLOv3, and YOLOv4. This study offers a reference for the IBD of substation personnel and provides an automated intelligent monitoring method.