An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission....

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Veröffentlicht in:	Agriculture (Basel) Jg. 12; H. 10; S. 1659
Hauptverfasser:	Jiang, Kailin, Xie, Tianyu, Yan, Rui, Wen, Xi, Li, Danyang, Jiang, Hongbo, Jiang, Ning, Feng, Ling, Duan, Xuliang, Wang, Jianjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.10.2022
Schlagworte:	Accuracy Algorithms Animal husbandry Animal welfare Aquatic birds Artificial intelligence attention mechanism Behavior Computer networks Construction industry Data mining Datasets Deep learning Ducks Efficiency Eggs Farms Feature extraction Hemp hemp duck count Labeling Livestock Machine learning Methods object detection Object recognition Performance evaluation Physiology Poultry Poultry industry Poultry production Waterfowl YOLOv7 China
ISSN:	2077-0472, 2077-0472
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Abstract	Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting.
AbstractList	Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting.
Audience	Academic
Author	Li, Danyang Xie, Tianyu Duan, Xuliang Wang, Jianjun Feng, Ling Jiang, Kailin Yan, Rui Jiang, Ning Wen, Xi Jiang, Hongbo
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SubjectTerms	Accuracy Algorithms Animal husbandry Animal welfare Aquatic birds Artificial intelligence attention mechanism Behavior Computer networks Construction industry Data mining Datasets Deep learning Ducks Efficiency Eggs Farms Feature extraction Hemp hemp duck count Labeling Livestock Machine learning Methods object detection Object recognition Performance evaluation Physiology Poultry Poultry industry Poultry production Waterfowl YOLOv7
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Title	An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation
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