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

Kailin Jiang, Tianyu Xie, Rui Yan, Xi Wen, Danyang Li, Hongbo Jiang, Ning Jiang, Ling Feng, Xuliang Duan and Jianjun Wang ()
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Kailin Jiang: College of Science, Sichuan Agricultural University, Ya’an 625000, China
Tianyu Xie: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Rui Yan: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Xi Wen: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Danyang Li: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Hongbo Jiang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Ning Jiang: College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Ling Feng: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Xuliang Duan: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jianjun Wang: College of Science, Sichuan Agricultural University, Ya’an 625000, China

Agriculture, 2022, vol. 12, issue 10, 1-18

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.

Keywords: object detection; YOLOv7; attention mechanism; deep learning; hemp duck count (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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