Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception

• URL: http://arxiv.org/abs/2310.01449v2
• Date: Wed, 3 Apr 2024 15:32:17 GMT
• Title: Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception
• Authors: Yaxin Feng, Yuan Lan, Luchan Zhang, Guoqing Liu, Yang Xiang,
• Abstract summary: A topology-aware energy loss function-based network training strategy named EIEGSeg is proposed. EIEGSeg is designed for multi-class segmentation on real-time traffic scene perception. Our results demonstrate that EIEGSeg consistently improves the performance, especially on real-time, lightweight networks.
• Score: 8.429178814528617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban segmentation and lane detection are two important tasks for traffic scene perception. Accuracy and fast inference speed of visual perception are crucial for autonomous driving safety. Fine and complex geometric objects are the most challenging but important recognition targets in traffic scene, such as pedestrians, traffic signs and lanes. In this paper, a simple and efficient topology-aware energy loss function-based network training strategy named EIEGSeg is proposed. EIEGSeg is designed for multi-class segmentation on real-time traffic scene perception. To be specific, the convolutional neural network (CNN) extracts image features and produces multiple outputs, and the elastic interaction energy loss function (EIEL) drives the predictions moving toward the ground truth until they are completely overlapped. Our strategy performs well especially on fine-scale structure, \textit{i.e.} small or irregularly shaped objects can be identified more accurately, and discontinuity issues on slender objects can be improved. We quantitatively and qualitatively analyze our method on three traffic datasets, including urban scene segmentation data Cityscapes and lane detection data TuSimple and CULane. Our results demonstrate that EIEGSeg consistently improves the performance, especially on real-time, lightweight networks that are better suited for autonomous driving.

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