Annotation Methods for Object Detection: A Comparative Analysis from Manual Labeling to Automated Annotation Technologies

With the rapid development of artificial intelligence technology, data annotation plays an increasingly important role in fields such as machine learning and deep learning. This article aims to comprehensively review the research status, applications, and challenges of data annotation tools and technologies, in order to provide a reference for researchers in related fields. Data annotation refers to the process of processing, organizing, and marking raw data to facilitate the learning and analysis of data by machine learning algorithms. This article first introduces the basic concepts of data annotation, then analyzes in detail the current mainstream data annotation tools and technologies, including manual annotation, semi-automatic annotation, and automatic annotation. Next, this article explores the applications of data annotation in various fields and points out the current challenges and future development trends.