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基于G-RepVGG和魚類運動行為的水質(zhì)監(jiān)測方法
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國家重點研發(fā)計劃項目(2020YFD0900201)


Water Quality Monitoring Based on Fish Movement Behavior and G-RepVGG
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    摘要:

    水質(zhì)惡化會直接造成水產(chǎn)養(yǎng)殖產(chǎn)量下降,,嚴重時會導致水產(chǎn)動物大量死亡,給養(yǎng)殖企業(yè)造成嚴重經(jīng)濟損失,。因此對水產(chǎn)養(yǎng)殖中水質(zhì)參數(shù)進行實時監(jiān)測具有重要意義,。本文以斑石鯛為研究對象,提出了一種基于魚類行為的水質(zhì)監(jiān)測方法,。該方法通過攝像機拍攝到的圖像數(shù)據(jù)就可以非侵入地完成水質(zhì)參數(shù)的實時監(jiān)測,,避免了安裝復雜設備、對魚類行為進行量化等繁瑣過程,。為了增加推理速度和降低模型參數(shù)量,,通過將RepVGG block與GhostNet相結(jié)合構建了G-RepVGG模型,使該模型更適用于移動設備的部署,。提出了計算量較少,、推理速度快、更適合水質(zhì)快速監(jiān)測的Cheap Ghost操作和計算量大,、精確率高,、更適合水質(zhì)的精確監(jiān)測Expensive Ghoost操作。由于多分支網(wǎng)絡適合進行訓練但是在推理速度上低于單分支網(wǎng)絡,,因此通過模型重參數(shù)化首先將卷積層以及批歸一化(Batch normalization, BN)層合并,,隨后再將3路卷積合并為1路,大大降低模型參數(shù)量,、提高了模型推理速度,,使模型更加適用于移動設備的推理。結(jié)果表明:使用Cheap Ghost操作的G-RepVGG在測試集中準確率達到96.21%,,圖像處理速度達到442.27f/s,,使用Expensive Ghost操作的G-RepVGG模型在測試集中準確率達到97.63%,,圖像處理速度達到349.42f/s,從而在保證較高精度的前提下依舊具有較高的推理速度,,在多個數(shù)據(jù)集中測試具有較好的魯棒性,。

    Abstract:

    Waterin aquaculture is a necessary place for aquatic animals to survive and live. The deterioration of water quality will directly lead to the decline of aquaculture production, and in severe cases, it will lead to the death of a large number of aquatic organisms and cause serious economic losses to aquaculture enterprises.Therefore, the real-time monitoring of water quality parameters in aquaculture is of great significance.A method for water quality monitoring based on fish behavior was proposed with Oplegnathus punctatus as research object.The method can non-invasively complete the real-time monitoring of water quality parameters through the image data captured by the camera, avoiding the tedious installation of complex equipment and the quantification of fish behavior.To increase the inference speed and reduce the amount of model parameters, this method combined RepVGG block with GhostNet.Aiming at the problems of rapid water quality monitoring and accurate water quality monitoring, the Cheap Ghost operation and the Expensive Ghost operation were proposed.Finally, the three branches were merged through model reparameterization, which greatly reduced the amount of model parameters and improved the model inference speed.The results showed that the G-RepVGG operated by Cheap Ghost achieved an accuracy of 96.21% in the test set and can infer 442.27 images per second. The G-RepVGG model operated with Expensive Ghost achieved 97.63% accuracy in the test set and can infer 349.42 images per second. Therefore, it still had a high inference speed under the premise of ensuring high accuracy, and had better robustness in testing in multiple data sets. The research result can quickly and accurately monitor water quality, detect water quality deterioration in time, and reduce losses caused by water quality deterioration, providing ideas and methods for water quality monitoring.

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孫龍清,王泊寧,王嘉煜,王新龍.基于G-RepVGG和魚類運動行為的水質(zhì)監(jiān)測方法[J].農(nóng)業(yè)機械學報,2022,53(s2):210-218. SUN Longqing, WANG Boning, WANG Jiayu, WANG Xinlong. Water Quality Monitoring Based on Fish Movement Behavior and G-RepVGG[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(s2):210-218.

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  • 收稿日期:2022-06-21
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  • 在線發(fā)布日期: 2022-08-16
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