Vitenskapelig Kapittel/Artikkel/Konferanseartikkel   2020

Chiu, Mang Tik; Xingqiang, Xu; Wang, Kai; Hobbs, Jennifer; Hovakimyan, Naira; Huang, Thomas S.; Shi, Honghui; Wei, Yunchao; Huang, Zilong; Schwing, Alexander; Brunner, Robert; Dozier, Ivan; Dozier, Wyatt; Ghandilyan, Karen; Wilson, David; Park, Hyunseong; Kim, Junhee; Kim, Sungho; Liu, Qinghui; Kampffmeyer, Michael C; Jenssen, Robert; Salberg, Arnt-Børre; Barbosa, Alexandre; Trevisan, Rodrigo; Zhao, Bingchen; Yu, Shaozuo; Yang, Siwei; Wang, Yin; Sheng, Hao; Chen, Xiao; Su, Jingyi; Rajagopal, Ram; Ng, Andrew; Huynh, Van Thong; Kim, Soo-Hyung; Na, In-Seop; Baid, Ujjwal; Innani, Shubham; Dutande, Prasad; Baheti, Bhakti; Talbar, Sanjay; Tang, Jianyu

Publikasjonsdetaljer

Sider:

212–218

År:

2020

Lenker:

ARKIV: hdl.handle.net/10037/20402
FULLTEKST: openaccess.thecvf.com/content_CVPRW_2020/html/w5/Chiu_The_1st_Agriculture-Vision_Challenge_Methods_and_Results_CVPRW_2020_paper.html
DOI: doi.org/10.1109/CVPRW50498.2020.00032

Del av: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 (IEEE, 2020)

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here.