UAV inspection is vital for ensuring the safe operation of new power systems, and visual object tracking technology can effectively improve inspection accuracy. Given the challenges of complex background and large target deformation in an actual electrical power scene, a segmentation and tracking method for UAV power equipment based on mask memory is proposed. First, an L2 mask memory matching method improves segmentation accuracy by calculating frame affinities using the negative squared Euclidean distance and uses historical mask information for target matching. Second, to reduce memory redundancy, a change-aware memory updating mechanism is introduced. The degree of target change is jointly assessed by image and mask, and a confidence threshold-based memory update strategy is designed. Finally, the segmentation strategy is combined with the DCF tracking framework to realize robust segmentation tracking of power equipment. The proposed method is evaluated on VOT2018 (generalized dataset), UAV123 (UAV dataset), and a real power scenario dataset. Experimental results demonstrate that the proposed method effectively enhances UAV inspection accuracy and provides valuable technical references for the safe operation of power equipment.