Railway Obstacle Intrusion Detection Based on Convolution Neural Network Multitask Learning
Haixia Pan, Yanan Li, Hongqiang Wang, Xiaomeng Tian
Abstract
The detection of train obstacle intrusion is very important for the safe running of trains. In this paper, we design a multitask intrusion detection model to warn of the intrusion of detected target obstacles in railway scenes. In addition, we design a multiobjective optimization algorithm that performs with different task complexity. Through the shared structure reparameterized backbone network, our multitask learning model utilizes resources effectively. Our work achieves competitive results on both object detection and line detection, and achieves excellent inference time performance (50 FPS). Our work is the first to introduce a multitask approach to realize the assisted-driving function in a railway scene.