深層学習で豚の顔から感情を正確に読み取る

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Deep learning strategies with CReToNeXt-YOLOv5 for advanced pig face emotion detection

Lili Nie, Bugao Li, Yihan Du, Fan Jiao, Xinyue Song & Zhenyu Liu

2024/01/09

https://doi.org/10.1038/s41598-024-51755-8

論文概要

本研究では、豚にとって表情は極めて重要な意味を持っており、感情や身体的な健康、意図などを判断するための高度なコミュニケーション手段となっていることを明らかにする。豚の顔面筋は構造的にあまり発達していないため、こうした表情を解読すること自体が容易ではない。このため本研究では深層学習を用いて豚の表情を認識する革新的モデル CReToNeXt-YOLOv5 を導入した。

ここで提案するモデルでは、検出精度と適応性を向上させるためにいくつかの改良が施されている。まず、損失関数をCIOUからEIOUへ移行することで、トレーニングのダイナミクスを最適化し、精度を重視した回帰結果が得られた。さらに、コーディネート・アテンション機構の組み込みにより、表情の複雑な特徴に対する感度が向上した。

CReToNeXt モジュールを統合したことは重要な革新であり、これによって微妙な表情を識別する能力が強化された。有効性試験の結果、CReToNeXt-YOLOv5は平均精度 89.4%を達成し、基盤となっている YOLOv5と比較して6.7%の大幅な向上が見られた。

とりわけ重要なのは、この技術的進歩がアニマルウェルフェアのモニタリングや研究に大きな影響を与えることである。本研究の結果は、深層学習モデルには豚の表情を認識する精度を革命的に向上させる能力があることを示しており、より人間的かつ情報に基づいた畜産管理の実践へと道を開くものである。

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