# 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. irregular behaviors; substation; ECA; gamma correction; MobileNetV3; improved YOLOv4 +
Footnote †: journal: Applied Sciences  
in substations. [PERSON] et al. [6] used the ViBe background modeling algorithm to detect and extract moving targets captured by the video surveillance system in a substation, then the histogram of oriented gradient (HOG) features was extracted to train the support vector machine (SVM) to achieve pedestrian classification, and finally safety helmet detection was performed through the color features. [PERSON] [7] and [PERSON] [8] divided worker images into three units, then extracted HOG and histogram of color (HOC) features from these three units to train a linear SVM classifier to improve the detection precision of substation workers' protective clothing. Traditional machine learning-based methods usually require image processing along with complex features definition, extraction, and selection processes. However, the detection accuracy of these methods in complex backgrounds such as uneven illumination, different weather, and unusual angles is often drastically reduced, and cannot meet the requirements of irregular behavior detection (IBD) of substation personnel. Recently, with the introduction of deep learning, the related technologies of object detection have developed substantially and the feature extraction ability has been significantly improved, avoiding the tedious process of manual feature definition through layer-by-layer feature extraction and nonlinear transformation. The development of deep learning object detection technology provides a new automatic detection method for image-based object detection of power equipment, ref. [9] personnel safety, foreign objects, ref. [10] etc., in substations. [PERSON] et al. [11] quickly identified substation operators by training a single shot multi-box detector (SSD) model, then combined this with the geometric features of the human body, HSV color space, and morphological processing to detect safety helmet wearing conditions. [PERSON] et al. [12] proposed an improved Faster R-CNN algorithm based on Resenet101; combined with Retinex image enhancement technology and K-means++ clustering algorithm, the mean average precision (mAP) for helmet wearing detection can reach 94.3% and the detection speed can reach 11.62 images per second. [PERSON] et al. [13] proposed an enhanced RCNN model which combined Faster RCNN and the Wasserstein generative adversarial network (GAN) to enhance low-resolution images of substations; the mAP and intersection over union (IoU) of people, bicycles, cars, and other targets in substations were 82.45% and 71.26%, respectively. [PERSON] et al.