Abstract
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.
Highlights
UAV inspection image is the most important information carrier in Industrial Internet of Things (IIoT)
The camera should focus on the transmission line equipment when the UAV takes photos during the line patrol, and the presence of raindrops will affect the camera’s focus, making the image background virtual, and the image detail information loss is serious, so the follow-up operation of the machine patrol image with raindrops will be extremely difficult
Through the research on the existing methods, we found that most of the traditional methods of raindrop removal are based on the model
Summary
UAV inspection image is the most important information carrier in Industrial Internet of Things (IIoT). Sometimes there are raindrops on the camera in the process of UAV line patrol, which will cover the information of the target object in the background image and reduce the image quality. The camera should focus on the transmission line equipment when the UAV takes photos during the line patrol, and the presence of raindrops will affect the camera’s focus, making the image background virtual, and the image detail information loss is serious, so the follow-up operation of the machine patrol image with raindrops will be extremely difficult. The existence of raindrops will lead to the uneven quality of the machine patrol image, which will affect the extraction and utilization of image information and reduce the accuracy and reliability of target detection
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