A new recognition algorithm for high‐voltage lines based on improved LSD and convolutional neural networks. Issue 1 (7th December 2020) - Luo, Yanhong; Yu, Xue; Yang, Dongsheng
Abstract: With the development of high‐voltage transmission and artificial intelligence technology, unmanned line inspection has become the inevitable trend of current electric power inspection. A new recognition algorithm for high‐voltage lines is proposed based on colour (Red, Green, Blue) RGB image to support the unmanned line inspection. Firstly, in order to solve the problem of missing weak edges in image edge detection, an improved Canny algorithm is proposed. Fourier transform Gaussian filter is introduced to enhance the high‐frequency signal of the image, which makes the extracted edge information more complete. At the same time, an improved line segment detector (LSD) algorithm is developed to extract the high‐voltage line. The complementary edge information of the three channels of the colour RGB image is analyzed, and the calculation formula of the horizontal line angle is improved, which greatly reduces the possibility of false detection and missed detection in the high‐voltage line extraction. In addition, the convolution neural network (CNN) is used to accurately recognize the extracted high‐voltage lines, which reduces the interference of non–high‐voltage lines. Simulation results show that the proposed algorithm has high recognition accuracy and strong robustness in the complex environment.