An efficient YOLO v3-based method for the detection of transmission line defects

The UAV inspection method is gradually becoming popular in transmission line inspection, but it is inefficient only through real-time manual observation. Algorithms are available to achieve automatic image identification, but the detection speed is slow, and video image processing is not possible. In this paper, we propose a fast detection method for transmission line defects based on YOLO v3. The method first establishes a YOLO v3 target detection model and obtains the a priori size of the target candidate region by clustering analysis of the training sample library. The training process of the model is accelerated by adjusting the loss function to adjust the learning direction of the model. Finally, transmission line defect detection was achieved by building a transmission line defect sample library and conducting training. The test results show that compared with other deep learning models, such as Faster R-CNN and SSD, the improved model based on YOLO v3 has a huge speed advantage and the detection accuracy is not greatly affected, which can meet the demand for automatic defect recognition of transmission line inspection videos

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber-physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method