分割
计算机科学
水准点(测量)
路面
对偶(语法数字)
人工智能
计算机视觉
任务(项目管理)
实时计算
工程类
地理
地图学
文学类
艺术
土木工程
系统工程
作者
Jian Sun,Junge Shen,Xin Wang,Zhaoyong Mao,Jing Ren
出处
期刊:IEEE transactions on intelligent vehicles
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:8 (2): 1549-1563
被引量:3
标识
DOI:10.1109/tiv.2022.3216734
摘要
Highway surface segmentation consists of extracting road surface at pixel-level from the surveillance camera view. Since the intelligent traffic event detection task does not require the detection of off-road scene, the segmentation of highway surface is of great demand. However, it is challenging to accurately extract road surface in real time scenarios. To cope with the above issues, Bi-Unet, a dual stream lightweight network is proposed. Firstly, the dual stream structure enhances segmentation performance on the narrow remote end of the highway and preserves the detailed border information. Then, to perform real-time segmentation, a novel lightweight module (LSM) is introduced to lighten the model and provide higher segmentation accuracy. Moreover, to ensure road segmentation for complex scenes, a Road Attention Network (RAN) module is proposed. Lastly, due to the lack of a suitable benchmark dataset serve for the highway segmentation problem, a new large and high-quality segmentation dataset named Highway-Surface-Free (HSF) is proposed in this paper, which is collected from the perspective of highway surveillance cameras under all-day and all-weather conditions. Compared with the state of arts, the extensive experimental results verify that our proposed Bi-Unet achieves the best overall performance on our proposed HSF dataset.
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