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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20687 |
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| _version_ | 1866915306112286720 |
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| author | Sun, Mingxuan Jiang, Juntao Yang, Zhiqiang Kong, Shenao Qi, Jiamin Shang, Jianru Luo, Shuangling Sun, Wanfa Wang, Tianyi Wang, Yanqi Wang, Qixuan Dai, Tingjian Chen, Tianxiang Zhang, Jinming Zhang, Xuerui He, Yuepeng Fu, Pengcheng Guan, Qiu Zhou, Shizheng Yu, Yanbo Jiang, Qigui Zhou, Teng Shi, Liuyong Yan, Hong |
| author_facet | Sun, Mingxuan Jiang, Juntao Yang, Zhiqiang Kong, Shenao Qi, Jiamin Shang, Jianru Luo, Shuangling Sun, Wanfa Wang, Tianyi Wang, Yanqi Wang, Qixuan Dai, Tingjian Chen, Tianxiang Zhang, Jinming Zhang, Xuerui He, Yuepeng Fu, Pengcheng Guan, Qiu Zhou, Shizheng Yu, Yanbo Jiang, Qigui Zhou, Teng Shi, Liuyong Yan, Hong |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images Sun, Mingxuan Jiang, Juntao Yang, Zhiqiang Kong, Shenao Qi, Jiamin Shang, Jianru Luo, Shuangling Sun, Wanfa Wang, Tianyi Wang, Yanqi Wang, Qixuan Dai, Tingjian Chen, Tianxiang Zhang, Jinming Zhang, Xuerui He, Yuepeng Fu, Pengcheng Guan, Qiu Zhou, Shizheng Yu, Yanbo Jiang, Qigui Zhou, Teng Shi, Liuyong Yan, Hong Computer Vision and Pattern Recognition 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/. |
| title | VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.20687 |