## 2 Method
### Dataset Construction and Process with Multi-Role Collaboration  
The dataset construction process plays a pivotal role in ensuring data quality and integrity, enhancing processing efficiency, and enabling scalable data expansion [31]. As illustrated in Figure 1, traditional dataset construction processes are typically designed for static datasets. When datasets undergo operations such as augmentation, addition, or deletion of data, annotation work must often be repeated from the beginning, leading to substantial redundant labor. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Role** & **Responsibility Description** \\ \hline Data Augmentation Personnel & Execute image rotation, flipping, and other augmentation operations, and generate augmentation rule files (such as rotation\_angle.txt) \\ \hline Automatic Annotation Personnel & Maintain the annotation algorithm library, and handle the automatic annotation of augmented images and label format conversion (such as YOLO\(\rightarrow\)COCO) \\ \hline \end{tabular}
\end{table} 
Table 1: Added roles and their responsibilities. A key limitation of the traditional process depicted in Figure 1 is that any expansion of the dataset or modifications such as the addition, removal, or alteration of data categories render the original annotation files invalid. This necessitates re-annotation using manual annotation tools. When a considerable volume of annotated data has already been accumulated, this repetitive work becomes particularly onerous and inefficient. This study adopts data annotation within a multi-person collaboration framework as an illustrative case and proposes a comprehensive data annotation process, as depicted in Figure 2.  
The dataset construction and annotation workflow follows these sequential steps: problem orientation, identification target classification (including classification and naming), original image collection and task assignment, image set naming and rule design, image review, data annotation, annotated data review, data augmentation, automatic annotation, augmented dataset naming and label generation, data review, automated data distribution, and final data output, as shown in Figure 2.  
1. Problem Orientation: The data annotation process begins with problem orientation. Establishing a dataset requires first identifying the approximate application scenario and the specific problem the dataset aims to address. Large-scale datasets can support multiple problem orientations, each corresponding to distinct application scenarios and target identifications. 2. Identification Target Determination and Classification: Based on the established problem orientation, the next step is to specify the targets to be identified and perform the relevant classification work. This process generates a correspondence table that links problem orientations with the classification of identification targets. 3. Data Collection: The data collection phase encompasses acquiring diverse types of data, including videos and images. Since collected data may contain issues such as missing values, noise, or duplicate entries, it is essential to perform data cleaning tasks to ensure the dataset comprises high-quality, reliable data. 4. File Naming by Classification: After filtering, the cleaned data are named according to the class name defined in the target classification correspondence table, followed by an image sequence number. Figure 1: A traditional workflow for static dataset construction. Key Step--Classification Modification Management: During the project, if deletions or modifications occur within the target classification scheme, the administrator pauses annotation tasks, issues an updated classification correspondence table, and employs a custom-developed tool to revise existing annotations accordingly. Once the updates are complete, annotators receive notifications to resume work following the new annotation guidelines, thereby preventing large-scale annotation errors stemming from data inconsistency. * 6.