An Efficient Network for Obstacle Detection in Rail Transit Based on Multi-Task Learning.
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2023)
Abstract
Obstacle detection, as one of the core functions of autonomous trains, consists of two specific tasks: track area segmentation and foreign object detection. Conventionally, the two tasks are performed separately. In contrast, we first propose a unified and efficient multi-task network to handle these tasks jointly. Specifically, a schema with one encoder for feature sharing and two decoders for specific tasks is adopted, and three designs such as effective Squeeze-Excitation module are proposed to obtain well shared representations. Furthermore, we establish a high-quality dataset by fusing RailSem19 and MRSI. Experiments demonstrate that our method obtains competitive performances on both track area segmentation and foreign object detection compared with state-of-the-art methods while far exceeding the existing approaches in terms of computational efficiency.
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Key words
Network Efficiency,Multi-task Learning,Obstacle Avoidance,Rail Transit,Computational Efficiency,Specific Tasks,Object Detection,Foreign Body,Object Segmentation,Segmentation Detection,Tracking Area,Multi-task Network,Superior Performance,Scaling Factor,Local Information,Feature Maps,Data Augmentation,Attention Mechanism,Stochastic Gradient Descent,Detection Model,Segmentation Task,Object Detection Model,Dilated Convolution,Semantic Segmentation,Dilation Rate,Feature Pyramid Network,Detection Task,Feature Extraction Capability,Object Tracking,Detection Head
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