Image denoising technology of power equipment based on deep residual network
Yong Chen, Wang Yun-Hui, Li Song, Xie Min, Yongkun Yang, Ke Zhang · 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) · 2022
The continuous development and expansion of the power system puts forward higher and higher requirements for the reliability of the power equipment. Therefore, the early detection and elimination of power equipment faults is very important, which can not only reduce equipment losses, but also avoid accidents to a certain extent. With the development of artificial intelligence technology, computer vision has begun to be widely used in power systems. For computer vision technology, high-quality images determine whether the identification of power equipment and defect detection technology can have high performance. The image is noisy. In order to solve the problem of noise interference in the visible light image of power equipment, this paper proposes a denoising method for the visible light image of power equipment based on deep learning network. First, the text analyzes the existing problems and requirements in power equipment image denoising, and proposes the overall system architecture of the study. Secondly, on the basis of analyzing the basic principle of residual network, the paper proposes a deep residual network for denoising of visible light images of power equipment, in which the improved activation function is used to improve the performance of the network, and the denoising experiments of visible light images of power equipment in this paper, It is proved that the proposed denoising method is better than the traditional method.