ECR-MobileNet: アダプティブ・コントラスティヴ・リグレッションと依存度グラフの切り取りによる不均衡な大口ベースパラメータ予測モデル
Hao Peng1, Cheng Ouyang1, Lin Yang1
1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Animals : an open access journal from MDPI
|August 28, 2025
まとめ
この研究は,水産物における非破壊的な魚の測定のための深層学習モデルであるECR-MobileNetを紹介しています. 魚の長さや体重を予測する上で高い精度を達成し, 縁部署には軽量です.
科学分野:
- 水産技術
- コンピュータビジョン
- ディープラーニング
背景:
- 魚の長さや体重を正確に 破壊しないように監視することは 賢い養殖に不可欠です
- 伝統的な方法はストレスと収穫の損失を引き起こし,現在のコンピュータビジョンモデルはデータ不均衡とモデルサイズに苦しんでいます.
研究 の 目的:
- 魚の精密で破壊的でない生物学的モニタリングのための効率的で堅固なディープラーニングの枠組みを開発する.
- 魚介類のコンピュータビジョンにおけるデータ不均衡とモデル軽量化の問題に対処する.
主な方法:
- 提案されたECR-MobileNetは,MobileNetV3-Smallに基づいた軽量なフレームワークです.
- 組み込みの効率的なチャネル注意 (ECA) モジュール,アダプティブマルチスケールコントラスティブ回帰 (AMCR) 損失関数,および依存度グラフベースの (DepGraph) 構造的剪定.
- マルチシーンの大口バスのデータセットで訓練され評価されています.
主要な成果:
- 改善されたECR-MobileNet-Pモデルは14のベンチマークを上回った.
- 低RMSEで0. 9784 (長さ) と0. 9740 (重量) のR2を達成した.
- モデルは非常に効率的です: 0.52Mパラメータ,0.07GFLOPs,10.19msのCPUレイテンシー,パレト最適性を実証しています.
結論:
- ECR-MobileNetは,水産生物学的モニタリングのためのエッジ展開可能なストレスのないソリューションを提供します.
- 不均衡回帰とタスク指向型モデルの圧縮のための革新的なアプローチを提示します.
- インテリジェントな水産養殖システムを発展させるための新しい方法論的パラダイムを確立する.
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