## 1 Introduction
In reality, the availability of large volumes of high-quality data and robust data-processing techniques is often the primary contributor to improved model outcomes. As a technical discipline, it is increasingly important to embrace an engineering-oriented and practical approach. Addressing problems through strategies aimed at lowering usage costs, enhancing training and inference efficiency, strengthening generalization capabilities, and reducing the thresholds and complexities of deployment is more meaningful and impactful [1]. A closer examination of existing challenges reveals that the application of computer vision technology in current vertical fields remains insufficiently developed. From an engineering perspective, there is a notable lack of systematic management methodologies and standardized processes for constructing large-scale dynamic datasets tailored to specific domains. Furthermore, there is a significant shortage of efficient automated annotation tools capable of accommodating dynamic changes within datasets. From a management perspective, the construction of datasets involves a complex project management process comprising numerous interconnected stages. Some teams lack the necessary experience and suitable platforms, which can result in project delays and the inability to meet established quality standards. Personnel management presents additional difficulties. Annotators are required to demonstrate advanced professional competencies, and the expectations placed upon them are stringent. Regarding quality, several key issues persist. First, annotation accuracy remains low due to disparities in annotator abilities, with some lacking adequate professional training. When confronted with complex data, such annotators are more susceptible to errors. Additionally, the limitations of annotation tools contribute to reduced accuracy, particularly when handling intricate datasets. Second, annotation consistency is often poor. Variations in annotators' interpretations and applications of standards can lead to inconsistent results for the same data batch, undermining overall data quality and complicating downstream processing. For instance, object boundary annotations frequently exhibit discrepancies among annotators. Furthermore, the absence of effective collaborative management mechanisms hampers the coordinated use of tools and reduces annotation efficiency. In terms of efficiency, traditional manual annotation methods [22; 23; 24; 25; 26; 27; 28; 29] continue to face significant challenges. Classic annotation tools currently in use include the following: (1) LabelImg [22], an open-source image annotation tool supporting basic shapes such as rectangles, commonly employed for object detection tasks. It allows annotation results to be saved in formats like PASCAL VOC and YOLO. Owing to its user-friendly interface, it is widely adopted among developers. (2) LabelMe [28; 29], initially developed by the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology, offers a broad range of annotation functionalities including instance segmentation, semantic segmentation, bounding box annotation, and image classification. It supports various annotation shapes such as rectangles, circles, line segments, and points, and can also annotate video data. (3) RectLabel [24], primarily designed for object detection and image instance segmentation annotation, supports output formats including YOLO, KITTI, COCO JSON, and CSV, and can read and write XML files in the PASCAL VOC format. This tool is widely utilized on Mac platforms. (4) Make Sense [24], recommended by the developers of YOLOv5 and Roboflow, is an open-source tool built using Typescript. However, it has not received code updates over the past two years, and the responsiveness to community inquiries remains relatively low. These annotation tools demonstrate clear limitations when confronted with dynamically evolving data.