分割
计算机科学
人工智能
计算机视觉
适应(眼睛)
领域(数学分析)
任务(项目管理)
遥感
地理
光学
工程类
物理
数学
数学分析
系统工程
作者
Junzhang Chen,Zichao Liu,Darui Jin,Yuanyuan Wang,Fan Yang,Xiangzhi Bai
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-08-11
卷期号:23 (12): 23194-23211
被引量:12
标识
DOI:10.1109/tits.2022.3194931
摘要
Semantic segmentation in urban scenes is widely used in applications of intelligent transportation systems (ITS). In urban scenes, thermal infrared (TIR) images can be captured in weak illumination conditions or in the presence of obscuration (e.g., light fog, smoke). Therefore, TIR images have great potential to endow automated intelligent vehicles or assist navigation systems. However, TIR imaging is blurry and low-contrast due to the absorption by atmospheric gases and heat transfer effect. Hence, TIR semantic segmentation in urban scenes has rarely been explored even though it has a wide range of scenarios in ITS. To overcome this limitation, we analyze the light transport of TIR light. Our analysis reveals that contours are the reliable features shared by TIR and Visible Spectrum (VS) light. Inspired by this, we attempt to transfer joint features from VS domain to TIR domain. Thus, we propose a curriculum domain adaptation method to guide the TIR urban scene semantic segmentation task from VS domain through contours. Moreover, to evaluate the proposed model, we build TIR-SS: an open-for-request dataset consisting of TIR images and pixel level annotations of 8 classes in urban scenes. Qualitative and quantitative experimental results on the dataset indicate that the proposed domain adaptation method outperforms related methods on this TIR semantic segmentation task.
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