构建微藻显微图像检测数据集，推动高通量细胞识别技术发展
VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images
- 构建含1000张图像的六类微藻数据集，覆盖大小差异与复杂背景
- 369支团队参与挑战，最优方法在小目标与模糊图像中表现优异
- 适合生态监测、生物成像与计算机视觉交叉研究者使用
微藻对生态平衡和经济领域至关重要，但因其形态多样、尺寸不一，在检测上存在挑战。本文总结第二届「视觉遇见微藻」（VisAlgae 2023）挑战赛，旨在提升高通量微藻细胞检测能力。挑战赛共吸引369支参赛队伍，提供包含1000张图像的数据集，涵盖六类微藻，具有不同尺寸和显著特征。任务包括小目标检测、运动模糊处理及复杂背景干扰应对。文中详述了排名前10的算法方案，揭示了克服上述挑战的关键思路，并显著提升了检测精度。该数据集与挑战为藻类研究与计算机视觉的融合提供了新范式，有助于深化生态认知与技术进步。数据集可于 https://github.com/juntaoJianggavin/Visalgae2023/ 获取。
原文摘要 · Abstract (English)
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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