The substation equipment is an important component of the power transmission and transformation process of the power grid. To ensure the normal operation of the power grid, it is necessary to diagnose faults in substation equipment, and accurate segmentation of substation equipment in infrared images is a key step in fault diagnosis. In response to the low segmentation accuracy and missed segmentation issues faced when segmenting substation equipment in complex scenes of infrared images, an instance segmentation algorithm for substation equipment in infrared images based on improved YOLOv8n is proposed. Firstly, a context-guided feature-enhanced down-sampling block is designed to replace the down-sampling convolutional layer in YOLOv8n, fully utilizing contextual and global information to enhance the model′s understanding of complex scenes. Then, the C2f module is introduced to reconstruct the deformable convolution in the Backbone, enhancing the extraction capability of irregular equipment features. Finally, the loss function is optimized using Wise-IOUv2 to improve the model′s generalization and classification ability. Using the infrared image dataset of substation equipment to experimentally verify the model, the experimental results show that compared to the YOLOv8n baseline model, the proposed method in this paper has increased mAP50 and mAP50:95 by 4.2% and 3.5% respectively. The proposed method can better solve the problem of missed segmentation of equipment in complex scenes and effectively improve the accuracy of substation equipment instance segmentation.