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VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images

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arxiv 2505.20687 v1 pith:YAVIL35T submitted 2025-05-27 cs.CV

VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images

Mingxuan Sun , Juntao Jiang , Zhiqiang Yang , Shenao Kong , Jiamin Qi , Jianru Shang , Shuangling Luo , Wanfa Sun
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Tianyi Wang Yanqi Wang Qixuan Wang Tingjian Dai Tianxiang Chen Jinming Zhang Xuerui Zhang Yuepeng He Pengcheng Fu Qiu Guan Shizheng Zhou Yanbo Yu Qigui Jiang Teng Zhou Liuyong Shi Hong Yan
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classification cs.CV
keywords detectionalgaechallengedatasetmicroalgaechallengesecologicalimages
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Microalgae, vital for ecological balance and economic sectors, present challenges in detection due to their diverse sizes and conditions. This paper summarizes the second "Vision Meets Algae" (VisAlgae 2023) Challenge, aiming to enhance high-throughput microalgae cell detection. The challenge, which attracted 369 participating teams, includes a dataset of 1000 images across six classes, featuring microalgae of varying sizes and distinct features. Participants faced tasks such as detecting small targets, handling motion blur, and complex backgrounds. The top 10 methods, outlined here, offer insights into overcoming these challenges and maximizing detection accuracy. This intersection of algae research and computer vision offers promise for ecological understanding and technological advancement. The dataset can be accessed at: https://github.com/juntaoJianggavin/Visalgae2023/.

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