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

• Published in: Agriculture (Basel) Vol. 12; no. 10; p. 1659
• Main Authors: Jiang, Kailin, Xie, Tianyu, Yan, Rui, Wen, Xi, Li, Danyang, Jiang, Hongbo, Jiang, Ning, Feng, Ling, Duan, Xuliang, Wang, Jianjun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: 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

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....