ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2009.07506
11
22

The 1st Tiny Object Detection Challenge:Methods and Results

16 September 2020
Xuehui Yu
Zhenjun Han
Yuqi Gong
Nan Jan
Jian-jun Zhao
QiXiang Ye
Jie Chen
Yuan Feng
Bin Zhang
Xiaodi Wang
Ying Xin
Jingwei Liu
Mingyuan Mao
Sheng Xu
Baochang Zhang
Shumin Han
Cheng Gao
Wei Tang
Lizuo Jin
Mingbo Hong
Yuchao Yang
Shuiwang Li
Huan Luo
Qijun Zhao
Humphrey Shi
ArXivPDFHTML
Abstract

The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection. The TinyPerson dataset was used for the TOD Challenge and is publicly released. It has 1610 images and 72651 box-levelannotations. Around 36 participating teams from the globe competed inthe 1st TOD Challenge. In this paper, we provide a brief summary of the1st TOD Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for researchers that areinterested in the TOD challenge. The benchmark dataset and other information can be found at: https://github.com/ucas-vg/TinyBenchmark.

View on arXiv
Comments on this paper