## 3 Experimental Setup  
### Experimental Design  
To comprehensively assess the performance of the vertical domain model dynamic dataset construction method based on multi-role collaboration and intelligent annotation, this study designed a series of rigorous experiments. Using the water conservancy engineering field as the application scenario, a specialized dataset was constructed, as illustrated in Figure 3. During the experiments, classification adjustments and data augmentation operations were performed, and comparisons were made with conventional data annotation methods.  
For dataset construction, the WATER-DET dataset was meticulously assembled to represent key scenarios in water conservancy engineering. The dataset comprises 1500 images encompassing 12 common water environment issues, including water pollution, abnormal water levels, and dam cracks. By annotating and analyzing these images, the dataset provides a robust data foundation to support vertical domain models in the water conservancy field. The initial classification included fifteen categories; however, as the experiments progressed and the need for model optimization emerged, certain categories were removed or added, and the number of images per category underwent multiple adjustments. The initial and final classifications are presented in Tables 5 and 6, respectively.  
Figure 3: Sample images from the WATER-DET dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Identification Target** & **Monitoring Content** & **Number of Samples** & **Remarks** \\ \hline
0 & Dog & Initial model training & 20 & To be deleted in the final classification \\
1 & Cat & Initial model training & 20 & To be deleted in the final classification \\
2 & Algae Pollution & River/lake water is greenish, with green algae & 150 & \\
3 & Oil Pollution & River/lake water shows signs of oil pollution & 20 & Fewer cases \\
4 & Red Pollution & River/lake water is reddish & 150 & \\
5 & Yellow Pollution & River/lake water is yellowish & 150 & \\
6 & Foam Pollution & River/lake water shows signs of foam pollution & 20 & Few original data \\
7 & Sewage & River/lake water is blackish or grayish & 150 & \\
8 & Human & Humans & & Original YOLOv1n category, deleted \\
9 & Garbage & Garbage on the water surface or along banks & 150 & \\
10 & Dead Fish & Floating fish on the water surface (mainly dead fish) & 80 & \\
11 & Leaves & Floating leaves on the water surface & 20 & Few original cases \\
12 & Sewage Outlets & Outlets, drainage pipes along banks & 150 & \\
13 & Sand Yard & Sand mining along banks & 150 & \\
14 & Buildings & Buildings along banks & 150 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial classification of the dataset.  
The methods developed in this study were applied and validated through experiments. For comparison, three popular annotation tools, LabellImg (Taipei, Taiwan, China), LabelMe (MIT CSAIL, Cambridge, MA, USA), and Make Sense (Melbourne, VIC, Australia), were selected. Evaluation was conducted using three key performance indicators: annotation efficiency, accuracy rate, and consistency. Annotation efficiency was measured by the number of images annotated per hour (images/hour), reflecting differences in annotation speed among the methods.  
To ensure experimental reliability and validity, all tests were performed under identical hardware and software environments. The hardware configuration included a high-performance server equipped with a multi-core processor, large-capacity memory, and high-speed storage devices to ensure computational performance and rapid data access. For software, a standardized operating system, a deep learning framework, and relevant dependent libraries were used to eliminate variations due to environmental discrepancies. Each method was tested multiple times, and average values were calculated to minimize experimental errors.