Direction Consistency-Guided Lightweight Power Line Detection Network for Aerial Images

Abstract

Accurate detection of power lines in aerial images is of great significance in ensuring grid security. However, complex power line scenarios and the thin and light structure of power lines both make it difficult to detect power lines accurately. Most of the existing approaches use traditional deep learning methods, using networks with a large number of parameters, computation, and memory occupation, thus making them not lightweight enough to perform on mobile devices. Based on this, a lightweight power line detection network based on direction consistency and location attention is proposed. The network is designed with a coordinate-aware feature extraction layer, which performs feature extraction by four-layer stacking to achieve faster inference speed while ensuring the network has fewer parameters. This layer is also able to sense the coordinates of the center pixel of the convolution in the image during the convolution process, thus preserving the location information of the power lines. In order to enhance the power line representation, a two-stage context-guided module is later utilized to simultaneously learn local features, surrounding context, and global context. Then, the features are input into a Gaussian kernel estimation module and features are aggregated in the corresponding directions through Gaussian kernels of eight different directions. The main directions of the power lines in the image and the corresponding Gaussian convolution kernels are obtained by filtering the feature responses. In addition, a kernel-guided decoder module is proposed to take advantage of the estimated power line features in the main direction of Gaussian kernel aggregation. This module can effectively enhance the power line representation and maintain the continuity of power lines. Meanwhile, low-level features are introduced to recover the edge details to realize high performance in distinguishing dense power lines. Both ablation experiments and comparison experiments on the transmission towers and power lines aerial-image and Power Line Aerial Image Dataset show that the proposed power line detection network has a good segmentation performance in complex scenarios. The proposed method performs the best in the comparison experiments, improving over the suboptimal method by 3.51% on average for the max F-measure metric.