The Application and Expansion of the YOLO Algorithm in the Field of Campus Cat and Dog Identification
Xingyu Pan, Wenjun Yue, Tonglin Liao, Xinyi Tang, Teoh Teik Toe · 2023
This paper explores the application of the YOLO algorithm for the recognition of campus cats and dogs. Traditional methods of animal identification are time-consuming and often inaccurate, making computer vision techniques a desirable alternative. The purpose of this research is to develop and evaluate a YOLO-based system for recognizing campus cats and dogs, with the goal of answering the following questions: can YOLO accurately recognize cats and dogs in campus environments, how does its performance compare to other object detection techniques, and what are its potential applications and limitations in real-world scenarios? The paper provides an overview of object detection techniques, the YOLO algorithm, and related research on campus animal recognition. It then describes the dataset and experimental design used in the study, the implementation and optimization of the YOLO algorithm, and the experimental results and analysis. Finally, the paper discusses the practical applications and potential extensions of the proposed system and concludes by summarizing the contributions, limitations, and future directions of this research.