Detection of bird nests in overhead catenary system images for high-speed rail

The high-speed rail system provides a fast, reliable and comfortable means to transport large number of travelers over long distances. The existence of bird nests in overhead catenary system (OCS) can hazard to the safety of the high-speed rails, which will potentially result in long time delays and expensive damages. A vision-based intelligent inspection system capable of automatic detection of bird nests built on overhead catenary would avoid the damages and increase the reliability and punctuality, and therefore is attractive for a high-speed railway system. However, OCS images exhibit great variations with lighting changes, illumination conditions and complex backgrounds, which pose great difficulty for automatic recognition. This paper addresses the problem of automatic recognition of bird nests for OCS images. Based on the unique properties of bird nests, we propose a novel framework, which is composed of five steps: adaptive binarization, trunk/branch detection, hovering point detection, streak extraction and pattern learning, for bird nest detection. Two histograms, Histogram of Orientation of Streaks (HOS) and Histogram of Length of Streaks (HLS), are novelly proposed to capture the distributions of orientations and lengths of detected twig streaks, respectively. They are modeled with Support Vector Machine to learn the patterns of bird nests. Experiments on different high-speed train lines demonstrate the effectiveness and efficiency of the proposed work.