## 1 Introduction
Manual annotation not only demands considerable time and financial resources but also fails to accommodate rapidly changing classification requirements. Additionally, manual processes are susceptible to subjective biases, which compromise the consistency and accuracy of annotation outcomes. When dataset tasks or data are updated, substantial repetitive work is often required to maintain dataset integrity. In response to these challenges, intelligent annotation technologies have been developed. For instance, automatic annotation variants of LabelImg are typically enhancements or extensions of the original LabelImg tool. Some notable examples include the following: (1) AutoLabelImg1, an improved version of LabelImg, which operates by training a model using a set of pre-annotated images and then integrating the trained model into the annotation tool to automatically process the remaining unlabeled data. The model's detection outputs can be converted into annotation files such as.xml or.voc formats. Although this tool significantly increases annotation efficiency, manual verification and correction of automatically generated annotations remain necessary. (2) LabelGo-YOLOv5 AutoLabelImg,a semi-automatic annotation tool built upon LabelImg and YOLOv5, utilizes pre-trained YOLOv5 model running on the PyTorch 1.7.0 framework to semi-automatically annotate datasets. After the user reviews and confirms the relevant information and selects the desired model, automatic annotation is executed, followed by the option to adjust and save the annotations according to specific project requirements. At present, several modern AI-powered solutions have substantially advanced automation capabilities, leading to significant improvements in work efficiency. Examples include Supervisely, Roboflow, and Scale AI. Among these, Supervisely has demonstrated notable progress in managing dynamic classification changes and implementing automatic data augmentation techniques. These intelligent annotation tools enable automatic data labeling, markedly increasing both annotation efficiency and accuracy. Despite these advantages, current intelligent tools exhibit certain limitations. First, they struggle to adequately address research tasks requiring dynamic classification adjustments and practical applications that vary with temporal and spatial factors. Second, existing intelligent annotation systems encounter challenges when dealing with evolving classification demands and complex geometric transformations. When confronted with new classification requirements or shifts in data distribution, it is often necessary to retrain the model or manually revise annotation strategies. Moreover, during data augmentation processes, re-annotation is typically required. For example, when objects in an image undergo rotation, the annotation boxes may become misaligned, resulting in inaccurate labels. These challenges collectively reduce annotation efficiency and consistency, significantly limiting the flexibility and practical utility of intelligent annotation tools. In light of the aforementioned challenges, this paper proposes several innovative solutions. First, it introduces a novel multi-person collaboration model. In addition to the traditional roles of annotators, reviewers, and administrators, the model incorporates personnel dedicated to data augmentation and automatic data annotation. This addition reduces the operational burden on general annotators while enhancing both the quality and efficiency of the annotation process. Second, it optimizes the dataset construction workflow by presenting a comprehensive process encompassing problem identification, target classification, data collection and cleaning, annotation, review, data augmentation, automatic annotation, re-review, data distribution, and final output. Each component of this process is tightly integrated, ensuring effective collaboration and maintaining high data quality, thereby facilitating the seamless progression of annotation work and providing robust datasets for model training.