Research on image enhancement processing method of electric power equipment by introducing YOLO-v4 target detection model

A target detection model based on the upgraded version of YOLO-v4 is proposed to address the problems in image recognition of power engineering equipment, such as poor visual effects, large recognition gaps, and inconsistent image styles. A target detection dataset of infrared temperature difference photos of power engineering equipment was created, and the MSR_CR method was employed to enhance the contrast and clarity of the images in obscured weather conditions. The Focal_loss function is employed to address the classification challenges arising from the unequal recognition of image data. The experimental results show that after iteration to about 5000 times, the model performance stabilises, the loss factor converges to about 0.2, and the recognition accuracy is close to 100%. The image enhancement method is tested to achieve homogeneous recognition of multiple types of power engineering equipment with 96.31% recognition accuracy and 71 frames/second detection rate. The experiment verifies the scientificity of the novel enhancement recognition method.