The fault of the power grid transmission line itself or the foreign matter caught in it will pose a potential threat to the power system. Efficient anomaly detection is the key to maintain the stability of modern transmission systems. At present, the increasing demand for edge computing equipment makes it a trend to develop lightweight and efficient power grid anomaly detection methods. To deal with the practical demands of power grid anomaly detection, this paper brings the LPGANet, a lightweight model designed to achieve high accuracy while enhancing detection efficiency. The model is integrated with dynamic snake convolution (DSConv) and spatial-channel reconstruction convolution (SCConv) to increase multi-scale feature extraction and fusion and cut computational cost. In addition, An EMA method is adopted to enhance the concentration on foreground and reduce the impact of background. We release a new dataset containing 6,200 images of typical power grid anomalies such as broken strands, scattered strands, and other floating suspensions. The experimental results on the dataset demonstrate that LPGANet achieves the best accuracy, highest efficiency, and best comprehensive performance compared with other object detection methods. In addition, the effectiveness of the system under computational resource limitation is also verified in the deployment on Jetson AGX Orin edge devices.
Li et al. (Wed,) studied this question.