## 4 Experiments and Results
### Ablation Experiment  
#### 4.4.1 Module Ablation Experiment  
To evaluate the effectiveness of each module, ablation studies were conducted on the CrowdHuman dataset. By sequentially adding each module and comparing evaluation metrics before and after their integration, we comprehensively assessed the performance of network components. All ablation experiments were performed by training models on the CrowdHuman training set and validating them on the validation set. The results in Table 2 clearly demonstrate the effectiveness of the proposed method, showing significant improvements in AP and MR\({}^{-2}\).  
In our experiments, we adopted YOLOv8-L as the baseline algorithm and conducted ablation studies by incrementally integrating proposed modules. To validate the effectiveness of the original backbone, we replaced the CSPDarknet backbone of YOLOv8-L with ResNet50, referred to as \"Baseline (ResNet50)\". This modification resulted in a decrease in performance, with AP dropping from 87.7% to 87.2%, MR\({}^{-2}\) increasing from 42.9% to 43.6%, FLOPs rising from 165.2 to 177.9, and FPS dropping from 59.7 to 52.7. These results indicate that CSPDarknet is more suitable than ResNet50 for the YOLOv8 architecture in terms of detection accuracy and efficiency, especially in dense pedestrian scenarios. Compared to ResNet, CSPDarkNet has lower computational complexity and fewer parameters, enabling faster training and inference speeds. Consequently, all subsequent ablation studies were carried out on the official YOLOv8-L implementation. Replacing the original C2f module in YOLOv8 with the C2f-D2 CN module significantly improved both average precision (AP) and miss rate (MR\({}^{-2}\)). The C2f-D2 CN module enhances the network's receptive field through stacked deformable convolution modules while reducing parameters, leveraging skip connections to extract richer multi-scale pedestrian features.  
Further incorporating the multi-scale linear attention module (MLSAM) increased AP by 1.8% and reduced MR\({}^{-2}\) by 1.4%, validating its effectiveness. The MLAM captures global contextual information through line attention mechanisms and strengthens local feature processing via spatial enhancement, mitigating missed or false detections across scales.  
Adding the small-target detection head (SPDH) improved AP by 1.6% and MR\({}^{-2}\) by 0.8%. Subsequent integration of DFL-SloU further boosted AP by 1.0% and reduced MR\({}^{-2}\) by 1.2%. These ablation experiments collectively demonstrate the efficacy of the proposed components. Compared to the baseline, our final algorithm achieved a 5.6% AP gain and 4.2% MR\({}^{-2}\) reduction, substantially enhancing detection capability in dense scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **AP** & **MR\({}^{-2}\)** & **GFLOPS** & **FPS** \\ \hline Baseline & 87.7 & 42.9 & 165.2 & 59.7 \\ Baseline (ResNet50) & 87.2 & 43.6 & 177.9 & 52.7 \\ Baseline + C2f-D2 CN & 88.9 & 42.1 & 167.1 & 59.1 \\ Baseline + C2f-D2 CN + MLA & 90.2 & 41.3 & 167.6 & 58.7 \\ Baseline + C2f-D2 CN + MLSA & 90.7 & 40.7 & 168.2 & 58.5 \\ Baseline + C2f-D2 CN + MLSA + SPDH & 92.3 & 39.9 & 168.8 & 58.2 \\ Baseline + C2f-D2 CN + MLSA + SPDH + & 93.3 & 38.7 & 169.3 & 57.9 \\ DFL-SIOU(Ours) & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Module validation: ablation experiment results on module validation AP (%) and MR\({}^{-2}\)%. Baseline (ResNet50) refers to replacing the CSPDarknet backbone of YOLOv8-L with ResNet50, MLA represents the multi-scale line attention, MLSA represents the multi-scale line spatial attention module, and SPDH refers to the small-scale pedestrian detection head.