An Improved YOLOv4 Model for Object Detection of Bird Species Threatening Transmission Line Security

Abstract

Bird activities seriously affect the safety of power lines. An improved lightweight YOLOv4 model was proposed to recognize typical bird species threatening power transmission line security in this study. A dataset composed of 3000 images about 10 bird species that easily cause transmission line outages was constructed. An improved YOLOv4 model was established by replacing the feature extraction network with GhostNet. The focus layer was added in GhostNet, and the standard convolution in the path aggregation network (PANet) was replaced with the depthwise separable convolution (DSC), thus to greatly reduce the number of parameters in the model. After model training, the improved YOLOv4 was applied to detect bird targets in 300 test sample images. The experimental results indicate that the mean average precision (mAP) and frames per second (FPS) of the proposed model are respectively 97.55% and 43, which is much faster than YOLOv4. In terms of detection accuracy and efficiency, the proposed model was compared to the existing models such as SSD and YOLOv4. This study can be applied for bird recognition and therefore contribute to achieve differentiated prevention of bird-related outages.