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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.12476 |
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| _version_ | 1866910466887909376 |
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| author | Liu, Weizhen Tan, Jiayu Lan, Guangyu Li, Ao Li, Dongye Zhao, Le Yuan, Xiaohui Dong, Nanqing |
| author_facet | Liu, Weizhen Tan, Jiayu Lan, Guangyu Li, Ao Li, Dongye Zhao, Le Yuan, Xiaohui Dong, Nanqing |
| contents | Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotype (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotypes measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/Fish-Phenotype-Detect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12476 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture Breeding Liu, Weizhen Tan, Jiayu Lan, Guangyu Li, Ao Li, Dongye Zhao, Le Yuan, Xiaohui Dong, Nanqing Computer Vision and Pattern Recognition Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotype (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotypes measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/Fish-Phenotype-Detect. |
| title | Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture Breeding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.12476 |