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MDSNet: a lightweight network for real-time vision task on the unmanned mobile robot

文献类型: 会议论文

第一作者: Yingpeng Dai

作者: Yingpeng Dai 1 ; Junzheng Wang 2 ; Jing Li 2 ;

作者机构: 1.Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China

2.School of Automation, Beijing Institute of Technology, Beijing, China

关键词: Semantic segmentation;Roads;Feature extraction;Real-time systems;Inference algorithms;Data mining;Mobile robots

会议名称: International Symposium on Intelligent Robotics and Systems

主办单位:

页码: 21-25

摘要: To makes a trade-off between accuracy and inference speed for semantic segmentation, Multi-Scale Depthwise Separation network (MDSNet) is designed to be effective both in terms of accuracy and inference speed. This network extract local information and contextual information jointly and has feature maps with high spatial resolution. Compared with state-of-the-art algorithms, MDSNet achieves 66.57 MIoU on Camvid with only 0.5M parameters and 79.4 FPS inference speed on a single GTX 1070Ti card. Besides, MDS is deployed on the unmanned platform to test performance under different conditions. The results show that the proposed algorithm performs well on real-time applications in the real world.

分类号: tp242.6-53

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