Efficient Parallel Branch Network With Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation

Abstract

Image-based segmentation of overhead power lines is critical for power line inspection. Real-time segmentation helps the inspection robot avoid obstacles or land on the wire during the inspection task. It is challenging for several studies to achieve real-time overhead power line segmentation with high accuracy. In addition, cluttered background brings great difficulties to overhead power lines segmentation. To address these issues, an efficient parallel branch network for real-time overhead power line segmentation is proposed. Our framework combines a context branch that generates useful global information with a spatial branch that preserves high-resolution segmentation details. The asymmetric factorized depth-wise bottleneck (AFDB) module is designed in the context branch to achieve more efficient short-range feature extraction and provide a large receptive field. Furthermore, the subnetwork-level skip connections in the classifier are proposed to fuse long-range features and lead to high accuracy. Experiments demonstrate that our framework achieves more than 90% segmentation accuracy.