Rui Jian
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Rui Jian has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Guangbo Li
College of Electronic and Information Engineering, Huaibei Institute of Technology, Huaibei 235000, China
1 shared publication - Guolong Shi
School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
1 shared publication - Xie Jun
College of Command and Control Engineering, Army Engineering University of the People’s Liberation Army, Nanjing 210007, China
1 shared publication
Latest Publications
- Open Access
A Review of You Only Look Once Algorithms in Animal Phenotyping Applications
Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evolution of YOLO algorithms, (2) datasets and preprocessing methodologies, (3) application domains of YOLO algorithms, and (4) future directions. This paper aims to offer readers fresh perspectives and insights into animal phenotyping research.
Open AccessA Review of You Only Look Once Algorithms in Animal Phenotyping Applications
Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evolution of YOLO algorithms, (2) datasets and preprocessing methodologies, (3) application domains of YOLO algorithms, and (4) future directions. This paper aims to offer readers fresh perspectives and insights into animal phenotyping research.