Infrared fault image recognition is an important method to diagnose electrical equipment， but the recognition relies on the manually created bounding boxes over objects. In this paper， in order to improve the detection efficiency， automatic semantic segmentation of infrared images is investigated to recognize one or more electrical equipment objects. The proposed method is based on Mask-RCNN which has demonstrated good performance on instance segmentation. Our main contribution is applying transfer learning to Mask-RCNN， where importance sampling and parameter mapping are conducted to alleviate the data-shortage problem on pixel-level annotating. Experimental results on real-world datasets have shown that the improved version of Mask-RCNN is able to extract the shapes of electrical equipment， even with limited data with pixel-level annotations. The proposed algorithm provides an efficient way to the subsequent steps of fault region detection and classification.