Image Detection of Foreign Body Intrusion in Railway Perimeter Based on Dual Recognition Method

Abstract

In order to ensure the safety of railway operation, it is urgent to strengthen the detection and protection of railway perimeter safety. This paper proposes a method for detecting foreign body intrusion in railway perimeter based on double recognition. The Gaussian Mixture Model (GMM) is used to process the video image of the railway scene, and the foreign objects are pre-screened, and the foreign object existence frames are extracted, and then the YOLOv3 algorithm is used to perform secondary detection and recognition on the foreign object existence frames. This method can improve the accuracy of target recognition, reduce the false alarm rate and false alarm rate of foreign object invasion in railway scenes, and occupy less on-site computing resources, which is suitable for on-site requirements. The results show that, compared with the GMM, the false negative rate of the algorithm in this paper is lower, and the algorithm is more suitable for railway site requirements than the deep learning algorithm.