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基于Clite-YOLOv5的雞狀態(tài)檢測算法
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青島科技大學(xué) 自動(dòng)化與電子工程學(xué)院

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TP391.41;TG441.7?

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國家自然科學(xué)基金(62003183)。


Clite-YOLOv5 Based Chicken State Detection Algorithm
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    摘要:

    異常雞的及時(shí)發(fā)現和處理能夠極大地避免規模化養殖中傳染病的傳播,異常雞常見(jiàn)的表征狀態(tài)為雞閉眼或瞇眼。為實(shí)現肉雞養殖過(guò)程中問(wèn)題雞只的實(shí)時(shí)監測,提出一種基于YOLOv5的雞狀態(tài)檢測算法Clite-YOLOv5,該算法以YOLOv5為基礎,進(jìn)行了如下改進(jìn):提出融合了CBAM的lite-CBC3模塊并用其重構了YOLOv5主干網(wǎng)絡(luò ),提高了復雜背景下小目標的檢測能力;采用改進(jìn)后的Fuse-NMS抑制算法,降低了檢測框的誤刪率并微調最終檢測框;采用深度可分離卷積替換主干網(wǎng)絡(luò )中的普通卷積,減少了模型的參數量,使模型更易在移動(dòng)端部署。實(shí)驗結果表明,提出的Clite-YOLOv5算法均值平均精度(mAP)為92.88%,視頻幀率為92FPS,性能超過(guò)現有其他算法,能夠滿(mǎn)足雞狀態(tài)實(shí)時(shí)監測的需求。

    Abstract:

    The timely detection and treatment of abnormal chickens can greatly avoid the spread of infectious diseases in large-scale farming, and the common characterization state of abnormal chickens is that they close their eyes or squint. To achieve real-time monitoring of problem chickens in broiler farming, a YOLOv5-based chicken status detection algorithm Clite-YOLOv5 is proposed, which is based on YOLOv5 with the following improvements: the lite-CBC3 module incorporating CBAM is proposed and used to reconstruct the YOLOv5 backbone network to improve the detection capability of small targets in complex backgrounds; The improved Fuse-NMS suppression algorithm is used to reduce the false deletion rate of detection frames and fine-tune the final detection frames; the depth-separable convolution is used to replace the normal convolution in the backbone network, which reduces the number of parameters of the model and makes the model easier to deploy on mobile. Experimental results show that the proposed Clite-YOLOv5 algorithm has a mean average precision (mAP) of 92.88% and a video frame rate of 92 FPS, which outperforms other existing algorithms and can meet the demand for real-time monitoring of chicken status.

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初曉玉,祖麗楠.基于Clite-YOLOv5的雞狀態(tài)檢測算法計算機測量與控制[J].,2023,31(9):55-61.

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  • 收稿日期:2023-03-20
  • 最后修改日期:2023-03-30
  • 錄用日期:2023-03-30
  • 在線(xiàn)發(fā)布日期: 2023-09-18
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