Abstract: The accuracy of substation equipment extraction in Infrared images directly affects the results of fault diagnosis. To solve the problem that the edge segmentation of substation equipment in the complex infrared background is imprecise and the segmentation accuracy is low, an infrared image segmentation method for substation equipment based on improved Mask R-CNN model was proposed. Firstly, the standard convolution of some residual modules in the ResNet feature extraction network was replaced with a deformable convolution. Then the spatial attention mechanism module and the channel attention mechanism module were connected in parallel, and deformable convolution was added to both modules. Finally, the loss function of the Mask R-CNN mask branch was improved to further optimize the fineness of target edge segmentation. The method can effectively improve the model’s ability to adapt to the diversity of geometric features of substation equipment in infrared images and alleviate the model’s focus on interference features such as the background. Experimental validations were carried out on the dataset of infrared images of substation equipment and the results show that AP_50:95, AP₅₀ and AP₇₅ improved by the method in this paper are respectively 3.5%, 1.0%, and 4.2% higher than that improved by the Mask R-CNN benchmark model. It is shown that the method can significantly improve the accuracy of the segmentation of substation equipment in infrared images and effectively solve the problem of imprecise edge segmentation.