The 1st Tiny Object Detection Challenge:Methods and Results

TL;DR

The paper summarizes the first Tiny Object Detection Challenge, highlighting methods, results, and the released TinyPerson dataset aimed at improving tiny object detection accuracy.

Contribution

It introduces the TinyPerson dataset and provides a comprehensive overview of the challenge, including top methods and results, fostering research in tiny object detection.

Findings

Multiple teams participated, showcasing diverse approaches.

The top methods achieved significant improvements in tiny person detection.

The dataset and challenge platform are publicly available for future research.

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.