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.

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With the popularization of unmanned aerial vehicle (UAV) applications and the continuous development of the power grid network, identifying power line scenarios in advance is very important for the safety of low-altitude flight. The power line scene recognition (PLSR) under complex background environments is particularly important. The complex background environment of power lines is usually mixed by forests, rivers, mountains, buildings, and so on. In these environments, the detection of slender power lines is particularly difficult. In this paper, a PLSR method of complex backgrounds based on the convolutional capsule network with image enhancement is proposed. The enhancement edge features of power line scenes based on the guided filter are fused with the convolutional capsule network framework. First, the guided filter is used to enhance the power line features in order to improve the recognition of the power line in the complex background. Second, the convolutional capsule network is used to extract the depth hierarchical features of the scene image of power lines. Finally, the output layer of the convolutional capsule network identifies the power line and non-power line scenes, and through the decoding layer, the power lines are reconstructed in the power line scene. Experimental results show that the accuracy of the proposed method obtains 97.43% on the public dataset. Robustness and generalization test results show that it has a good application prospect. Furthermore, the power lines can be accurately extracted from the complex backgrounds based on the reconstructed module.