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| Format: | Preprint |
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2024
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| Accès en ligne: | https://arxiv.org/abs/2403.07153 |
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| _version_ | 1866916156845064192 |
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| author | Chen, Leo Boardley, Benjamin Hu, Ping Wang, Yiru Pu, Yifan Jin, Xin Yao, Yongqiang Gong, Ruihao Li, Bo Huang, Gao Liu, Xianglong Wan, Zifu Chen, Xinwang Liu, Ning Zhang, Ziyi Liu, Dongping Shan, Ruijie Che, Zhengping Zhang, Fachao Mou, Xiaofeng Tang, Jian Chuprov, Maxim Malofeev, Ivan Goncharenko, Alexander Shcherbin, Andrey Yanchenko, Arseny Alyamkin, Sergey Hu, Xiao Thiruvathukal, George K. Lu, Yung Hsiang |
| author_facet | Chen, Leo Boardley, Benjamin Hu, Ping Wang, Yiru Pu, Yifan Jin, Xin Yao, Yongqiang Gong, Ruihao Li, Bo Huang, Gao Liu, Xianglong Wan, Zifu Chen, Xinwang Liu, Ning Zhang, Ziyi Liu, Dongping Shan, Ruijie Che, Zhengping Zhang, Fachao Mou, Xiaofeng Tang, Jian Chuprov, Maxim Malofeev, Ivan Goncharenko, Alexander Shcherbin, Andrey Yanchenko, Arseny Alyamkin, Sergey Hu, Xiao Thiruvathukal, George K. Lu, Yung Hsiang |
| contents | This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of computer vision (CV) on edge devices. Most CV researchers focus on improving accuracy, at the expense of ever-growing sizes of machine models. LPCVC balances accuracy with resource requirements. Winners must achieve high accuracy with short execution time when their CV solutions run on an embedded device, such as Raspberry PI or Nvidia Jetson Nano. The vision problem for 2023 LPCVC is segmentation of images acquired by Unmanned Aerial Vehicles (UAVs, also called drones) after disasters. The 2023 LPCVC attracted 60 international teams that submitted 676 solutions during the submission window of one month. This article explains the setup of the competition and highlights the winners' methods that improve accuracy and shorten execution time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 2023 Low-Power Computer Vision Challenge (LPCVC) Summary Chen, Leo Boardley, Benjamin Hu, Ping Wang, Yiru Pu, Yifan Jin, Xin Yao, Yongqiang Gong, Ruihao Li, Bo Huang, Gao Liu, Xianglong Wan, Zifu Chen, Xinwang Liu, Ning Zhang, Ziyi Liu, Dongping Shan, Ruijie Che, Zhengping Zhang, Fachao Mou, Xiaofeng Tang, Jian Chuprov, Maxim Malofeev, Ivan Goncharenko, Alexander Shcherbin, Andrey Yanchenko, Arseny Alyamkin, Sergey Hu, Xiao Thiruvathukal, George K. Lu, Yung Hsiang Computer Vision and Pattern Recognition This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of computer vision (CV) on edge devices. Most CV researchers focus on improving accuracy, at the expense of ever-growing sizes of machine models. LPCVC balances accuracy with resource requirements. Winners must achieve high accuracy with short execution time when their CV solutions run on an embedded device, such as Raspberry PI or Nvidia Jetson Nano. The vision problem for 2023 LPCVC is segmentation of images acquired by Unmanned Aerial Vehicles (UAVs, also called drones) after disasters. The 2023 LPCVC attracted 60 international teams that submitted 676 solutions during the submission window of one month. This article explains the setup of the competition and highlights the winners' methods that improve accuracy and shorten execution time. |
| title | 2023 Low-Power Computer Vision Challenge (LPCVC) Summary |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.07153 |