Technical research on unmanned inspection of power grid equipment based on artificial intelligence technology

With the intelligent development of power systems, the importance of power grid equipment inspection has rapidly increased. Traditional inspection methods suffer from inefficiency, safety risks, and other issues. This paper proposes an unmanned inspection approach, exploring the application of optimized object detection algorithms in power inspections. By integrating two deep learning models Faster Region-based Convolutional Neural Network (R-CNN) and Swin Transformer, a Swin-Faster R-CNN model is constructed to combine their strengths for accurate detection of power equipment status, thereby enhancing computer vision and object detection capabilities. The original dataset is expanded with additional annotations, and experiments demonstrate that increased data diversity significantly improves the model's generalization ability. The fused model exhibits excellent performance in detection accuracy, processing speed, and complex scenario adaptability, with experimental results ultimately validating the effectiveness of this technology.