Transmission line ice monitoring technology based on microwave sensing

When ice covers power lines, it can cause safety problems in power transmission. To ensure stable operation and reliable power supply of power system, automatic filtering and icing detection are realized using automatic algorithm such as deep learning technology, so that time consumption and human resources are greatly reduced.Therefore, this study designed a transmission line ice covering detection network RPN-YOLOv8 based on improved YOLOv8. First, the network uses the receptor field attention convolution and coordinate attention to improve the C2f module of the network backbone, so as to improve the network's position information capture and eliminate the problem of convolutional parameter sharing. Secondly, for the detection head with a large number of parameters in the original network, lightweight heavy design is carried out to reduce the number of parameters and improve the detection speed on the basis of ensuring the detection accuracy. Finally, combining WIoU and normalized Gaussian Wasserstein distance reframe loss function wnwd is constructed to enhance the detection accuracy of small size targets. The experimental results show that RPN-YOLOv8's parameters are reduced 12.6% compared to YOLOv8's, while the average average precision improves to 74.8%, improving 3.7% compared to the improved algorithm.