To address the problem of low accuracy and slow recognition speed of infrared (IR) image target recognition of substation equipment in complex backgrounds, this study proposes an IR image recognition algorithm for substation equipment based on the improved YOLOv7-Tiny. First, the lightweight bottleneck structure GhostNetV2 bottleneck is introduced to replace a part of the CBS module and build a lightweight and efficient aggregation network known as a lightweight-efficient layer aggregation network. Simultaneously, a coordinate attention mechanism is embedded in the feature extraction stage to reduce the number of network parameters while strengthening the network's extraction of key features of the target and improving detection accuracy. The network coordinate loss function is replaced by SIoU_Loss to improve the anchor frame positioning accuracy and network convergence speed. The results show that the accuracy of the improved network is 96.28%, the detection rate is 26.42 frames/s, and the model size is reduced to 7.82 M. Compared with the original YOLOv7-Tiny algorithm, the detection rate is increased by 21.69%, the identification accuracy is improved, and the model size is reduced by 36.89%. These results meet the requirements of accurate real-time identification of substation equipment and lay a foundation for subsequent substation equipment fault diagnosis.