## 4 Experiments
### Experimental Results  
#### 4.3.1 Performance Evaluation of the CrowdHuman Dataset  
This experiment utilizes the CrowdHuman dataset to perform object detection experiments on the YOLOv8x-Improved model. The primary focus is to compare the detection performance of various models, including SSD, Faster-RCNN (ResNet + FPN), YOLOv7x, YOLOv8x, and YOLOv8x-Improved. Table 6 presents a comparative analysis of these models' performances on the CrowdHuman dataset.  
YOLOv8x-Improved significantly enhances key performance metrics. The Precision reaches 0.853, while the Recall stands at 0.772, demonstrating its exceptional capability in recognizing positive samples. In terms of the average precision, YOLOv8x-Improved achieves a score of 0.862 on mAP50 and 0.611 on mAP50-95, indicating its robustness across various IOU thresholds. Furthermore, the model size is reduced to 109 MB, with GFLOPS decreasing to 232.2, resulting in approximately an 18% reduction in weight due to the incorporation of the C2f module MCC. Regarding the processing speed, YOLOv8x-Improved excels with a performance rate of 48 frames per second, surpassing both the original YOLOv8x and other models. This demonstrates significant improvements in speed while maintaining high precision and recall rates.  
Combining all performance metrics, the YOLOv8x-Improved model exhibits a more pronounced performance compared to the original YOLOv8x (Figure 15). The enhanced model consistently demonstrates improvements in both precision and recall, ultimately achieving higher steady-state values and enhancing the detection of true-positive samples. In the evaluation of the mean average precision (mAP_0.5), the improved model surpasses its predecessor during the later stages of training while maintaining an exceptional performance on the more rigorous mAP_0.5:0.95 metrics. This indicates substantial advancements in its object detection capabilities.  
In terms of training and validation loss, YOLOv8x-Improved employs the WIOU loss function in place of the original CIOU loss (Figure 16). The experimental results indicate that YOLOv8x-Improved exhibits a lower bounding box loss compared to YOLOv8x during both the training and validation phases, thereby significantly enhancing the target localization accuracy. Both models demonstrate a comparable performance regarding category loss, reflecting robust generalization capabilities. The effectiveness of the WIOU loss function further contributes to the improved performance of YOLOv8x-Improved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP50** & **mAP50-95** & **Model Size** & **GFLOPS** & **FPS** \\ \hline SSD & 0.661 & 0.614 & 0.638 & 0.482 & 105.1 M & 15.0 & 43 \\ Faster-RCNN & 0.684 & 0.621 & 0.662 & 0.524 & 57.22 M & 134.5 & 18 \\ ResNet + FPN & 0.842 & 0.721 & 0.845 & 0.581 & 135 M & 188 & 39 \\ YOLOv8x & 0.847 & 0.769 & 0.851 & 0.603 & 130 M & 258.1 & 40 \\ YOLOv8x-Improved & 0.853 & 0.772 & 0.862 & 0.611 & 109 M & 232.2 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of detection results on the CrowdHuman dataset.  
Figure 16: Training and validation loss curves.  
Figure 15: Comparison of training results between YOLOv8x-Improved and YOLOv8x.