Automatic Power Transmission Towers Detection Based on the Deep Learning Algorithm

Electric power construction is related to the national economy and people’s livelihood. However, some natural disasters, such as typhoons and earthquakes, make the power poles and transmission towers of the distribution network prone to damage. In this context, we use advanced deep learning algorithm to realize automatic detection of power transmission towers. Specifically, we show fast and accurate detection of power transmission towers from video frames taken by unmanned aerial vehicles (UAVs). First of all, we film the power transmission towers using UAVs, and make the datasets through the manual annotation method of video frame taking. Then, an efficient intersection over union (EIoU) is introduced to calculate the loss of predicted box and an activation function Mish is used to replace original activation function ReLU base on YOLO-V4 algorithm. Finally, the center line of the power transmission tower can be obtained by using ResNet-50 to locate its endpoints. Combined with the center line and detected box, the tilt angle of the tower can be calculated. Via testing and comparison, our algorithm can give consideration to both speed and accuracy, which is shown to be more suitable to be applied in power grid disaster survey as compared to other approaches. We believe that this method will play a positive role in the future detection of damages in power transmission towers.