The visible light images of electrical equipment in substation inspection are characterized by background clutter and irregular target contours, causing poor equipment segmentation accuracy and affecting the equipment recognition effect of intelligent inspection systems. This paper proposes an improved YOLACT++ model to realize accurate instance segmentation of equipment targets. First, the electrical equipment feature extraction backbone network DAGNet is designed to improve the network′s attention to important features in the complex background. Simultaneously, the 3D attention module SimAM is introduced in the prototype network branch to reduce the interference of the chaotic background on target segmentation. The model is validated using a labeled dataset of 1 730 visible images of six types of electrical equipment, including surge arresters and circuit breakers, obtained from inspections of 58 110 kV substations and 86 35 kV substations in eight regions of a city. The experimental results show that the APall index of the improved YOLACT++ model segmentation is 84.1%. It is 4.4% higher, and with YOLACT, Mask R-CNN, and YOLOv8 models, it is 4.0%, 9.3%, and 1.6% higher, better realizing the recognition of the six types of electrical equipment. The accuracy and rapidity of electric power inspection are met.