To address the problems of complicated data and low detection accuracy for deep-learning target detection of IR images of power equipment in complex environments, this study proposes a convolutional block attention module (CBAM) based on YOLOv7 to improve the classification algorithm for IR images of power equipment. First, the existing dataset is labeled and divided into training, validation, and test sets in a certain proportion and then introduced into the backbone network of YOLOv7 to enable the model to emphasize the region of interest and suppress useless information. Second, the divided dataset is put into the improved YOLOv7 for model training, and six improved YOLOv5s models are compared. The experimental results show that the improved YOLOv7 model outperforms YOLOv7, YOLOv5s, and six attention models based on YOLOv5s under the same experimental conditions. The improved YOLOv7 exhibits significantly improved performance and achieves fast and accurate IR image classification.