An Effective Method for Sensing Power Safety Distance Based on Monocular Vision Depth Estimation

As an important index of risk protection, the safety distance is crucial to ensure the safe and stable operation of the power system and the safety of personnel's life. Traditional monitoring methods are difficult to balance recognition accuracy and convenience. Therefore, this paper presents a power safety distance sensing method based on monocular visual images to achieve the recognition of the safety distance of external damage in complex scenes of transmission corridors, and proposed a power density depth distance model. In this model, a codec network with skip-connection to extract features and aggregate shallow and deep features for input power system images. Then, the regularization method, migration learning strategy, cosine annealing learning strategy, and data enhancement strategy are used to further optimize the model, so as to obtain a model with good accuracy and generalization in complex conditions. The effectiveness and superiority of the proposed method are verified in comparison to other external damage monitoring methods. The experimental results showed that the proposed method has high accuracy for the distance of external damage in the actual scenario. Moreover, the method has good generalizability, which can be easily deployed in video monitoring systems on different transmission corridors.