## 3 Experimental Setup
### Collaborative Workflow  
The collaborative workflow proposed in this study is illustrated in Figure 4, specifically under scenarios involving changes in classification order. This workflow can be activated under several scenarios, including but not limited to the following:  
1. When the problem domain evolves, necessitating the addition or removal of classifications;
2. When doubts arise regarding the rationality of the current classification system or a reset is required during model training; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Identification Target** & **Monitoring Content** & **Number of Samples** \\ \hline
0 & Dog & Test example & 20 \\
1 & Garbage & & \\
2 & Leaves & Floating leaves on the water & 80 \\  & & surface & \\
3 & Dead Fish & Floating fish on the water & 80 \\  & & surface (mainly dead fish) & 80 \\
4 & Sewage Outlets & Outlets, drainage pipes along & 150 \\  & & banks & \\
5 & Sewage & River/lake water is blackish or & 150 \\  & & grayish & 150 \\
6 & Algae Pollution & River/lake water is greenish, & 150 \\
7 & Human & & 80 \\
8 & Dead Animal & & 50 \\
9 & Oil Pollution & River/lake water shows signs of & 80 \\  & & oil pollution & 80 \\
10 & Red Sewage & River/lake water is reddish & 150 \\
11 & Horse & & \\
12 & Foam Pollution & River/lake water shows signs of & 150 \\
13 & Yellow Pollution & River/lake water is yellowish & 150 \\
14 & Sand Yard & Sand mining along banks & 150 \\
15 & Buildings & Buildings along banks & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Final classification of the dataset. When model evaluation indicates that uneven image distribution is adversely affecting performance;  
When certain images yield suboptimal training results, requiring targeted data augmentation;  
When new images from practical applications need to be integrated into the dataset. In the role-collaborative process, upon detection of classification changes, annotation tasks are immediately suspended, and a classification change correspondence table is generated and distributed. Data Augmentation Phase: Data augmentation personnel first perform comprehensive processing of the original data using techniques such as image rotation, scaling, cropping, and noise addition. These operations generate a diverse set of new images with characteristics similar to the original data but containing varied details. For image recognition tasks, rotating and scaling original images at multiple angles improves model exposure to diverse features, enhancing generalization capabilities. Data augmentation personnel also conduct preliminary screening and organization of augmented images to ensure data quality and usability. Review Phase: Reviewers play a vital role in maintaining annotation quality. They meticulously examine annotations produced by automatic annotation tools, checking for accuracy, consistency, and completeness. By comparing annotations across different annotators, reviewers identify and resolve discrepancies, ensuring data quality aligns with model training requirements. Additionally, reviewers refine and update annotation specifications based on observed challenges, promoting standardization and consistency in annotation practices. When issues are detected in annotation results, reviewers return the data to automatic annotators for correction or directly coordinate with data augmentation personnel to regenerate affected data. Once the review is successful, the dataset is dynamically updated with the revised data, ensuring the model benefits from the most current and accurate information. Moreover, based on feedback from model training outcomes and evolving project requirements, data augmentation personnel can adjust augmentation strategies, automatic  
Figure 4: The collaborative workflow for managing classification changes. annotators can optimize annotation algorithms, and reviewers can update review protocols, creating a closed-loop process for continuous improvement and dynamic dataset updates.