Power Line Extraction Framework Based on Edge Structure and Scene Constraints

Abstract

Power system maintenance is an important guarantee for the stable operation of the power system. Power line autonomous inspection based on Unmanned Aerial Vehicles (UAVs) provides convenience for maintaining power systems. The Power Line Extraction (PLE) is one of the key issues that needs solved first for autonomous power line inspection. However, most of the existing PLE methods have the problem that small edge lines are extracted from scene images without power lines, and bringing about that PLE method cannot be well applied in practice. To solve this problem, a PLE method based on edge structure and scene constraints is proposed in this paper. The Power Line Scene Recognition (PLSR) is used as an auxiliary task for the PLE and scene constraints are set first. Based on the characteristics of power line images, the shallow feature map of the fourth layer of the encoding stage is transmitted to the middle three layers of the decoding stage, thus, structured detailed edge features are provided for upsampling. It is helpful to restore the power line edges more finely. Experimental results show that the proposed method has good performance, robustness, and generalization in multiple scenes with complex backgrounds.