Transmission line detection based on a hierarchical and contextual model for aerial images

Abstract

Due to highly cluttered backgrounds and limited visual features, transmission line detection (TLD) in complex and natural outdoor environments is a difficult task. Motivated by image parsing, a TLD algorithm that utilizes a decomposition structure and contextual information is proposed. Considering that cables are generally associated with the presence of pylons, correlations between pylons and cables, as well as correlations among different cable segments, are defined as the contextual information, which is described using a hierarchical model with the structure of an and-or graph. First, pylons in images are detected via an active basis model, which is an appearance model that uses sparse representation. Next, according to the number of pylons, the bottom-up inference in the hierarchical model selects different procedures and corresponding operations to detect cables. Multiple experiments are performed on aerial images captured in practical powerline inspection tasks. The experimental results illustrate that our TLD algorithm can effectively manage images captured under various landforms at different visual distances and from different viewpoints. For highly cluttered environments in particular, our algorithm has obvious advantages in terms of detection performance compared with existing approaches.