## 3 Materials and Methods
### Detection of Dry Beach Area of Tailing Ponds
#### 3.2.3 Model Performance Evaluation Index  
Indicators such as precision (P), recall (R), average precision (AP), and mean average precision (mAP) are essential for assessing the model training results in this study. These indicators are an aid to the evaluation of the accuracy and overall performance of the model [43; 44].  
Precision (P) is an important metric for assessing the performance of a model as it measures the predictive accuracy of the model by calculating the ratio of true positives to the total number of positive cases predicted by the model. TP (True Positive) represents the scenario where the predicted and actual values are both true. Conversely, FP (False Positive) signifies cases where the predicted value is true while the actual value is false. Lastly, FN (False Negative) denotes instances where the predicted value is false despite the actual value being true.  
Recall (R), also referred to as sensitivity, is a crucial metric used to assess the model's ability to correctly identify positive cases. It measures the percentage of true positive cases correctly detected relative to the total number of true positives.  
Average precision (AP) is a fundamental indicator for assessing model performance, calculated as the area under the precision-recall curve. AP is a thorough evaluation of model performance, as it considers the complete precision-recall (P-R) curve and assesses the balance between precision and recall comprehensively. Higher AP values verify the superior performance of the model, indicating great precision and recall. A greater average precision (AP) number signifies a superior model performance, indicating high precision and high recall. The calculation formula is as shown in Equation (3) [38].  
\[\text{AP}=\int_{0}^{1}\text{P}(\text{R})\text{dR} \tag{3}\]  
The mean average precision (mAP) is the average of the AP scores for all categories. Assuming there are \(n\) categories of targets to be detected in the dataset, and the AP of the target in the first category is \(\text{AP}_{1}\), then the last category is \(\text{AP}_{n}\), and the average AP of these categories is mAP. The mAP metric is used to test the comprehensive detection ability of the model [45]. [EMAIL_ADDRESS] represents the average accuracy at an IOU threshold of 0.5. A higher [EMAIL_ADDRESS] value signifies better detection accuracy of the object detection model. mAP is a common statistic used to assess the performance of object identification algorithms across several item categories. The calculation formula is as shown in Equation (4).  
\[\text{mAP}=\frac{\text{AP}_{1}+\text{AP}_{2}+\cdots+\text{AP}_{n}}{\text{n}}= \frac{\sum_{i=1}^{n}\text{AP}_{i}}{\text{n}} \tag{4}\]  
This study primarily aims to detect the dry beach of tailings pond, specifically focusing on a singular category. In Formula (4), the category is one, thus n = 1. Therefore, when the IOU threshold is consistent, the mAP value of the research model equals the AP value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Details** \\ \hline CPU & Intel Core i7-13700H (Intel, Santa Clara, CA, USA) \\ GPU & NVIDIA GeForce RTX 4060 (NVIDIA, Santa Clara, CA, USA) \\ Memory & 32G \\ Image size & 640 \(\times\) 640 \\ Batch size & 16 \\ Learning rate & 0.01 \\ Epoch & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model training environment.