# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 4 Experiments
### Experimental Evaluation Index  
This paper evaluated the performance of the object detection model using precision (P), recall (R), and the mean average precision (mAP) as standards. These evaluation metrics provide a comprehensive understanding of the model's capabilities, allowing for a more thorough assessment of its effectiveness in the object detection task.  
Precision refers to the ratio of correctly predicted positive samples to all samples predicted as positive by the model. Its mathematical expression is as Equation (19).  
\[P=\frac{TP}{TP+FP} \tag{19}\]  
\(TP\) represents true positive, indicating the number of samples correctly predicted as positive by the model, and \(FP\) represents false positive, indicating the number of samples  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Settings** \\ \hline GPU & NVIDIA GeForce RTX 3060 12 GB \\ CPU & Intel Core I7 12700 KF \\ Memory & 16 G, 2666 MHz \\ OS & Ubuntu 20.04 LTS \\ CUDA & 11.2 \\ PyTorch & 1.10.1 \\ Python & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameter** & **Setting** \\ \hline Input image size & \(640\times 640\) \\ Batch size & 16 \\ Epochs & 250 \\ Initial learning rate & 0.01 \\ Minimum learning rate & 0.0001 \\ Optimizer & SGD \\ Momentum & 0.937 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameter settings.  
incorrectly predicted as positive by the model. By calculating precision, we can assess the model's precision in recognizing targets.  
Recall is another key evaluation metric used to describe the ratio of correctly predicted samples among all actual positive samples in the entire dataset. Its mathematical expression is as Equation (20).  
\[R=\frac{TP}{TP+FN} \tag{20}\]  
\(FN\) represents false negative, indicating the number of positive samples that the model failed to predict correctly. Through the evaluation of recall, we gain insight into the model's coverage of true targets, i.e., its ability to discover all positive samples.  
The mean average precision is a comprehensive metric that assesses the model's ability to predict and locate targets, considering performance differences between different categories. Its calculation method is as Equation (21).  
\[mAP=\frac{1}{C}\sum_{c=1}^{C}AP_{c}, \tag{21}\]  
where \(C\) represents the number of target categories, and \(AP_{c}\) is the average precision for each category. The average precision (AP) is obtained by calculating the area under the precision-recall (P-R) curve. Specifically, for each category, the P-R curve is plotted, and the area under the curve is computed as the average precision for that category. Finally, the mAP is the average of the average precisions for all categories. Introducing mAP allows for a more comprehensive evaluation of the model's performance in multi-class object detection tasks, considering differences between different categories. This helps in gaining a deeper understanding of the model's real-world performance, guiding further improvement and optimization.