@inproceedings{1ceb1ae49b5d48028ae688763ed55306,
title = "MDSNet: A lightweight network for real-time vision task on the unmanned mobile robot",
abstract = "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.",
keywords = "lightweight network, semantic segmentation, unmanned mobile robot",
author = "Yingpeng Dai and Junzheng Wang and Jing Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022 ; Conference date: 14-10-2022 Through 16-10-2022",
year = "2022",
doi = "10.1109/ISoIRS57349.2022.00013",
language = "English",
series = "Proceedings - 2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--25",
booktitle = "Proceedings - 2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022",
address = "United States",
}