Infrared images of substation equipment taken by the inspection robot often have the characteristics of complex background,target overlap,truncation and small scale of distant targets,posing significant challenges to recognition.Therefore,an improved infrared image target recognition model for substation equipment with YOLOv8 so is proposed in this paper.Firstly,Soft_NMS is introduced to reduce the loss of overlapping targets.Secondly,a multi dimensional cooperative attention mechanism MCA is added to the neck network to focus on the relevant feature regions,and the mixed loss function of Inner IoU and Focal loss is adopted to enhance the model′s generalization ability to small scale targets and the attention to high quality anchor frames.Finally,the GhostNetV2 module is employed for lightweight design of the model.Experimental results show that compared with the YOLOv8n benchmark model,the mAP@0.5 of this model is increased by 4.9%,the model parameters are reduced by 36.2%,and the detection speed reaches 160.1 FPS,which effectively improves the recognition ability and lightweight level of the model,laying a foundation for subsequent fault diagnosis of substation equipment.