Transmission line detection using deep convolutional neural network

The bottom of the overhead transmission line is easily affected by external environmental factors such as temperature, wind, snow and so on, thus fluctuating up and down. There is a great significance of the timely monitoring of the bottom position of the transmission line for the safe and stable operation of the transmission line. Recent years, the computer vision researches based on deep learning develop rapidly and have achieved remarkable performance. In this paper, a deep convolutional neural network (CNN) is proposed to locate the bottom position of the transmission lines. The experiment shows that our network model attained a highest identification accuracy of 99.89%. Moreover, the experiment adopts four-way parallel processing technology. Compared with other traditional methods, our proposed method can save a lot of time and have high prediction accuracy.