A lightweight infrared target detection algorithm MEGI-YOLOv5 was proposed. The algorithm was based on the YOLOv5 model. Firstly， the backbone network was replaced with the lightweight Mobilenet-v3 network， and part of the CBL structure in the neck network was replaced with the deep separable convolution of the reciprocal residual structure. The C3 module was replaced by the combination of ordinary convolution and GhostConv to reduce the model parameters and calculation amount. Secondly， the Efficient Channel Attention （ECA） module was embedded in the neck network to improve the model's attention to the channel， so as to improve the model's feature extraction ability. The experimental results showed that compared with the YOLOv5 model， the number of parameters of the model was reduced by 22%， the detection speed was increased by 37%， and the detection accuracy of the model could reach 96.42%， which could meet the accuracy and real-time requirements of the identification of substation equipment categories and hotspots， and provide conditions for the subsequent timely detection of substation equipment faults.