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RTSNet: Real-Time Semantic Segmentation Network for Outdoor Scenes
Ma, Mingyu1; Zou FS(邹风山)1,2; Xu F(徐方)1,2,3; Song JL(宋吉来)2,3
Department其他
Conference Name9th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2019
Conference DateJuly 29 - August 2, 2019
Conference PlaceSuzhou, China
Source PublicationProceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
PublisherIEEE
Publication PlaceNew York
2019
Pages659-664
Indexed ByEI ; CPCI(ISTP)
EI Accession number20201908641775
WOS IDWOS:000569550300113
Contribution Rank3
ISBN978-1-7281-0769-1
Keywordsemantic segmentation real-time outdoor scenes RTSNet mean intersection-over-union
AbstractSemantic segmentation technique plays an important role in robotics related applications, especially autonomous driving and assisted driving. Real-time semantic segmentation has very significant practical meaning, but many studies focus on accuracy, not computationally efficient solutions. In this paper, a real-time semantic segmentation network based on encoder-decoder architecture is proposed. This framework's encoder part adopted a lightweight network architecture for feature extraction and this architecture is mainly based on the MobilenetV2. Its decoder part is decided to use the Skip architecture. This architecture can utilize higher resolution feature mapping to provide adequate accuracy and greatly improve computational efficiency. We evaluated RTSNet on the Cityscapes dataset for urban scenes and compared with the state of the art real-time semantic segmentation networks. The mean intersection-over-union it can achieve on the Cityscapes dataset is about 62.0%, while it achieved 14.0 fps on NVIDIA Jetson TX2 with 360×640 input images.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26836
Collection其他
Corresponding AuthorMa, Mingyu
Affiliation1.Northeastern University, Shenyang 110819, China
2.Shenyang SIASUN Robot Automation Co. Ltd., Shenyang 110168, China
3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 10016, China
Recommended Citation
GB/T 7714
Ma, Mingyu,Zou FS,Xu F,et al. RTSNet: Real-Time Semantic Segmentation Network for Outdoor Scenes[C]. New York:IEEE,2019:659-664.
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