A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images

Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.