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The reliability of electrical components affects the stable operation of the power system. Electrical components inspection has long been important issues in the intelligent power system. The main problems of traditional recognition methods of electrical components are low detection accuracy and poor real-time performance, which are challenging to extract necessary features from the inspection images. This paper proposes a way to detect the electrical components in the Unmanned Aerial Vehicle (UAV) inspection image based on You Only Look Once (YOLO) V3 algorithm. Due to some of the inspection images are not clear, which result in the reduction of the available dataset. On this basis, we adopt Super-Resolution Convolutional Neural Network (SRCNN) to realize super-resolution reconstruction on the blurred image, which achieves the expansion of the dataset. We compare the performance of the proposed method with other popular recognition methods. The results of experiment verify the effectiveness of the proposed method, and the technique reaches high recognition accuracy, good robustness, and strong real-time performance for UAV power inspection system.

Aiming at the problem of getting low resolution image and blurred image during unmanned aerial vehicle (UAV) inspection, a super-resolution reconstruction algorithm based on very deep network super resolution (VDSR) is proposed. The algorithm model is composed of deep convolution neural network and residual structure, which is improved on the basis of super resolution convolutional neural network (SRCNN). By deepening the network to 20 layers, the receptive field can be expanded, and the residual structure can be combined to obtain better reconstruction effect. The experimental results show that the proposed super-resolution method based on VDSR has richer texture and more realistic visual effect, with 2.95 dB and 0.037 improvement in PSNR and SSIM compared with the super-resolution methods based on Bicubic Interpolation, Sparse Coding and SRCNN. The proposed algorithm further promotes the theoretical research of inspection image super-resolution, and can be effectively applied to the practical application of power inspection.