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基于改進(jìn)MobileNetV3的籠養(yǎng)蛋雞聲音分類識別方法
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農(nóng)業(yè)生物育種國家科技重大專項(2023ZD0407106)


Method for Sound Classification and Recognition for Caged Laying Hens Based on Improved MobileNetV3
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    摘要:

    為實現(xiàn)籠養(yǎng)蛋雞聲音的準(zhǔn)確分類,,實現(xiàn)蛋雞健康、情緒,、生產(chǎn)狀態(tài)等信息的智能化,、非接觸式檢測,提出了一種基于改進(jìn)MobileNetV3的籠養(yǎng)蛋雞聲音分類識別方法,。以欣華二號蛋雞為研究對象,,采集蛋雞在籠養(yǎng)條件下發(fā)出的熱應(yīng)激聲、驚嚇聲,、產(chǎn)蛋聲以及鳴唱聲,,經(jīng)過聲音預(yù)處理將一維聲音信號轉(zhuǎn)化為三維梅爾頻譜圖,建立了包括8541幅梅爾頻譜圖的蛋雞聲音數(shù)據(jù)集,。通過在MobileNetV3中引入高效通道注意力(Efficient channel attention, ECA)模塊,,提高了籠養(yǎng)蛋雞聲音分類準(zhǔn)確率。試驗結(jié)果表明,,MobileNetV3-ECA模型準(zhǔn)確率,、召回率、精確率以及F1分?jǐn)?shù)分別達(dá)到95.25%,、95.16%,、95.02%、95.08%,,相比原始模型分別提高1.99,、2.08,、2.00、2.04個百分點,。通過與分別引入坐標(biāo)注意力(Coordinate attention, CA),、卷積塊注意力模塊(Convolutional block attention module, CBAM)的模型對比,引入ECA模塊后模型準(zhǔn)確率分別提高2.11,、2.03個百分點,,其他指標(biāo)同樣有更明顯的提高。與ShuffleNetV2,、DesNet121和EfficientNetV2模型相比,,MobileNetV3-ECA準(zhǔn)確率分別提高1.99、2.03,、2.50個百分點,。本文提出的基于MobileNetV3-ECA的蛋雞聲音分類識別方法,能夠有效且準(zhǔn)確地實現(xiàn)對包括熱應(yīng)激聲在內(nèi)的不同種類蛋雞聲音分類識別,,為蛋雞規(guī)?;B(yǎng)殖中的自動化、智能化聲音檢測提供了算法支持,,為禽舍巡檢機器人功能優(yōu)化提供了參考,,同時為規(guī)模化籠養(yǎng)蛋雞熱應(yīng)激預(yù)警開辟了思路,。

    Abstract:

    In order to achieve accurate classification of caged laying hens-sounds and intelligent, non-contact detection of laying hens-health, emotion, production status and other information, a caged laying hens-sound classification and recognition method based on improved MobileNetV3 was proposed. The heat stress sound, fright sound, egg-laying sound and singing sound produced by laying hens under cage conditions were collected from Xinhua No.2 laying hens as research object, the one-dimensional sound signals were transformed into three-dimensional Mel-spectrograms after sound pre-processing, and the laying hens’sound data set consisting of 8541 Mel-spectrograms was established. The accuracy of sound classification for caged laying hens was improved by introducing the efficient channel attention (ECA) module in MobileNetV3. The experimental results showed that the MobileNetV3-ECA model achieved 95.25%, 95.16%, 95.02% and 95.08% of accuracy, recall, precision and F1 score, representing an enhancement of 1.99, 2.08, 2.00 and 2.04 percentage points, respectively, in comparison with the original model. Comparing the models with the introduction of coordinate attention (CA) and convolutional block attention module (CBAM) respectively, the accuracy of the model was improved by 2.11 and 2.03 percentage points with the introduction of the ECA module. Significant improvements were also seen in other metrics. The accuracy of MobileNetV3-ECA was improved by 1.99, 2.03 and 2.50 percentage points compared with that of ShuffleNetV2, DesNet121 and EfficientNetV2. The MobileNetV3-ECA based sound classification and recognition method for laying hens proposed provided algorithmic support for automated and intelligent sound detection in the large-scale breeding of laying hens, and also provided a reference for the function optimization of poultry house inspection robots, and opened up a way of thinking for heat stress early warning of large-scale caged laying hens.

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衡一帆,盛哲雅,嚴(yán)煜,谷月,周昊博,王樹才.基于改進(jìn)MobileNetV3的籠養(yǎng)蛋雞聲音分類識別方法[J].農(nóng)業(yè)機械學(xué)報,2025,56(4):427-435. HENG Yifan, SHENG Zheya, YAN Yu, GU Yue, ZHOU Haobo, WANG Shucai. Method for Sound Classification and Recognition for Caged Laying Hens Based on Improved MobileNetV3[J]. Transactions of the Chinese Society for Agricultural Machinery,2025,56(4):427-435.

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  • 收稿日期:2024-03-04
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  • 在線發(fā)布日期: 2025-04-10
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