At present, the cable maintenance of power companies is completed manually. Manual maintenance not only has heavy workload and low efficiency, but also has serious secure issue. With the rapid development of machine vision and the wide application of robot technology in all walks of life, it has become an inevitable trend to apply these techniques to the automatic cable maintenance. This paper presents a method of binocular cable recognition and location based on the YOLACT model. Firstly, it uses the improved YOLACT network to recognize and segment dense cables in complex environments, then optimizes and extracts the edge of the cable segmentation image, and finally uses the obtained cable edge features to match the targets in the binocular image, so as to realize the recognition and location of cables in complex environments. Compared with the traditional YOLACT model, the correlation calculation method of cable candidate frame proposed in this paper can well solve the problems of missed detection and false detection when identifying dense cables, and improve the accuracy of cable identification.