## 2 Method
### Design of Automatic Annotation Tools  
Building upon the dataset construction process described above, highly automated and adaptable tools are essential to support efficient data annotation. This paper introduces two automatic annotation tools: (1) an image data classification modification tool--Biaogai Jingling--and (2) an automatic annotation tool for post-augmentation processing--Zhizeng Huigai. Design of the Image Data Classification Tool--Biaogai Jingling  
The design of the image data classification modification tool focuses on two main functionalities: (1) classification modification, implemented through an automatic image label adjustment tool, and (2) classification deletion, also handled through an automatic label adjustment mechanism. In practical annotation workflows, it is inevitable that additions, deletions, or modifications of classifications will be encountered. Such changes necessitate corresponding adjustments to existing annotated data. Traditional annotation tools rely on manual updates to reflect classification modifications, resulting in substantial inefficiencies, repetitive work, and increased labor costs. This study develops a universal algorithm for automatically modifying and deleting image data label classifications, enabling rapid and efficient updates. The basic principle of the image data classification modification algorithm involves several algorithmic concepts. The alteration of the classification list is a critical aspect. This change is not merely an information update but can restructure the classification order. For  
Figure 2: The proposed dataset construction process with multi-role collaboration. example, as shown in Table 2, image data initially classified in the order of algae, foam pollution, and garbage pollution may, due to new classification requirements, be reordered as floating objects, river color, and riverside buildings. When the classification order changes, a significant challenge arises: annotations of previously labeled images become misaligned because annotation information is tightly coupled with the original classification order. Once the order is altered, existing annotations can no longer accurately correspond to the revised classification structure. For example, an image initially labeled as \"River Color--Red\" may be incorrectly reassigned to another category under the new classification scheme, leading to data inconsistency and inaccuracies. To resolve this issue, this study proposes an effective solution that employs a correspondence table detailing the relationship between classifications before and after modification. Using this table, the algorithm automatically updates the label files, meticulously adjusting each annotation to align with the new classification system. This process ensures that image annotations remain consistent with the revised categories, preserving both data accuracy and usability. 1. Handling Changes in Classification Order: The correspondence table, provided before and after classification updates, serves as the foundation for the algorithm to automatically modify the label files, synchronizing annotations with the new classification order. 2. Managing Classification Deletions: The image data classification modification algorithm follows defined principles when addressing deleted classifications. Specifically, if a classification is removed, the associated label tag files of images labeled under that category are deleted, as the corresponding classification information no longer exists. Retaining these labels would cause data redundancy and confusion. Additionally, labels for categories positioned after the deleted classification are reassigned lower order numbers by decrementing their category indices by one. For instance, if the original category numbers are 2, 3, and 4, and category 2 is deleted, the remaining categories 3 and 4 are renumbered to 2 and 3, respectively. This approach preserves logical consistency in the classification order. 3.