The outlet recognition for large-scale cable laying has problems such as difficulty obtaining prior information, similarity in local recognition information, and low recognition accuracy, making it difficult for most outlet recognition based on real prior information for large-scale cable laying to proceed smoothly. In response to the above issues, this paper proposes an outlet recognition method based on deep learning and sensors for large-scale cable laying. A system of outlet recognition based on deep learning and sensors for large-scale cable laying is formed based on the self-labeling of outlet images, data augmentation of outlet features, and relative pose constraints. The feasibility and effectiveness of this technology have been verified by recognizing outlets at different locations in a large-scale cable laying scene. This technology can recognize and locate outlets, effectively solving the problem of misrecognition at different positions.