"基于YOLO的目标检测优化算法研究: 发展、实现和应用"

Abstract Object detection is a crucial task in computer vision with various applications, such as autonomous driving, surveillance systems, and image retrieval. The You Only Look Once (YOLO) algorithm has gained popularity due to its real-time processing capabilities and high accuracy. However, there is still room for improvement in terms of detection accuracy and efficiency. This study focuses on optimizing the YOLO algorithm for object detection. Chapter 1 discusses the importance of object detection and provides an introduction to the YOLO algorithm. The necessity of optimizing object detection is also highlighted in this chapter. Chapter 2 presents a review of existing object detection algorithms and the evolution of the YOLO algorithm through various improvements. Chapter 3 delves into the principles of optimizing object detection based on YOLO, including data preparation and annotation, algorithm implementation, and optimization techniques. Chapter 4 details the experimental setup and compares the results of the optimized algorithm with other existing methods. Chapter 5 explores the optimization of the algorithm on different datasets and its potential applications in various domains. In Chapter 6, the study concludes with a summary of the work done and offers insights into future research directions. Overall, this research aims to enhance the performance of the YOLO algorithm for object detection through optimization techniques. The findings of this study contribute to the advancement of computer vision technology and have potential implications for real-world applications.