An Object Detection Model for Electric Power Operation Sites Based on Federated Self-supervised Learning

In the various edge cloud devices within the power system, a large amount of discrete data is generated, with the most significant amount being image data captured by each construction site. However, due to distance, privacy protection, transmission loss, and other issues, it is difficult to gather these data together for use, resulting in the waste of valuable data resources. Even if these data are collected, annotating them one by one is challenging, leading to limited training effectiveness for the model. As a result, supervision of electric power operation sites still depends on manual remote viewing of surveillance video, which is inefficient and error-prone. To address these issues, this paper proposes the use of Federated Self-supervised learning for object detection at electric power operation sites. This method effectively utilizes limited visual data in each terminal scene to jointly train a neural network model, improving the model's recognition accuracy under a distributed learning framework, increasing data utilization, and ensuring data security. The proposed approach has the potential to enhance the efficiency of electric power production.