In this paper, we propose an improved YOLOv4 target detection model combined with an MSRCR image enhancement algorithm to mitigate the poor visual effect, large-scale difference, and unbalanced data category in infrared images of power equipment. First, we constructed a target detection dataset of infrared images of power equipment and applied the MSRCR algorithm to enhance the original infrared images and improve the low contrast and blur pixel of infrared images in rain and fog weather. This improves the detection ability of the model to power equipment in rain and fog weather. Second, a multi-scale convolution module was introduced in the YOLOv4 backbone network to obtain multi-scale features of input images using convolution kernels with different sizes to enhance the initial feature representation. To further improve the detection accuracy, the Focal loss function was used to solve the difficult problem of classification caused by unbalanced infrared image data. The test results show that the average recognition accuracy of the proposed method for eight power equipments is 96.31%, and the detection speed is 71 frame/s. The experimental results verify the effectiveness of the proposed method.