Abstract: In the face of problems such as low resolution and image blurring in drone inspection images, a super-resolution reconstruction method is proposed for drone inspection images using the theory of VDRCNN(Very Deep Residual Convolutional Neural Network). The algorithm model consists of a VDSR( Very Deep Network for Super-Resolution) and a residual structure. Based on the VDSR, the algorithm is improved by adding a residual structure to enhance convergence speed, while combining batch group normalization and Adam optimizer to achieve better reconstruction effects. On this basis, an electric power component detection dataset is constructed, and high-resolution reconstruction of blurred electric power component images is achieved by properly setting the network parameters. The experimental results show that the super-resolution method based on VDRCNN can reconstruct images with richer textures and more realistic visual effects, with improvements of 2. 95 dB and 3. 79% in peak signal-to-noise ratio and structural similarity respectively, compared to traditional detection methods. Therefore, the proposed VDRCNN-based super-resolution reconstruction method has certain potential application value in solving practical problems in power inspection.

Key words: deep learning; , super-resolution; , convolutional neural network; , power inspection; , inspection images