Transmission Tower and Power Line Detection Based on Improved Solov2

Aerial image detection of transmission towers and power lines in transmission lines is the key technology for unmanned aerial vehicles (UAVs) intelligent inspection, path planning, and obstacle avoidance. Different from previous transmission line detection methods, this work uses an instance segmentation algorithm to detect transmission towers and power lines. However, the aerial survey image's large size and high-resolution result in a low signal-to-noise ratio, while the high length-to-diameter ratio of power lines presents great challenges. To solve these issues, this article conducts instance segmentation of aerial survey images based on the improved Solov2 network. Specifically, the neck of the model has been replaced by path aggregation feature pyramid network (PaFPN) to enhance the feature fusion ability and reduce feature loss. The designed MaskIou branch processes feature relationships before and after the Mask branch of Solov2 and calculates the segmentation loss of each type of mask. Transfer Learning is adopted to pretrain the network and learn data from a single type of lightning conductor so that the network can better identify linear features with high length to diameter ratio that is difficult to recognize. Comparison tests with multiple instance segmentation networks show that the proposed improved network can achieve higher precision in the segmentation.