Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection – Towards Precise Fish Morphological Assessment in Aquaculture Breeding
CoRR(2024)
摘要
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.
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