An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network

Abstract

It is significant to detect railway intrusion for railway safety. Unlike indoors, the railway is a complex environment with many interferences leading to false positives in the intrusion detection algorithm. However, there is a lack of research on effective intrusion detection in complex railway scenes. To solve these problems, the paper proposes RailDet, an effective railway intrusion detection method using dynamic intrusion region and lightweight neural network, which has high accuracy, low false-positives and high speed. RailDet consists of two stages. Firstly, the intrusion localization algorithm is used to obtain dynamic intrusion regions. Secondly, the object recognition based on lightweight neural network processes intrusion regions to obtain the coordinates and category. Finally, RailDet is validated on Chinese railways with various weathers and locations, which achieves 96.90% accuracy, 0.24% false-positive rate and 0.043 s/image. Moreover, the proposed method is very meaningful for the application of deep learning and not limited to railway.