## 4 Experiments and Results
### Comparison with State-of-the-Art Methods  
To validate the effectiveness of the proposed algorithm, comparative experiments were performed on various subsets of the CrowdHuman dataset, using AP, recall, and MR\({}^{-2}\) as evaluation metrics. The results are summarized in Table 7.  
The CrowdHuman dataset, known for its complexity and diversity, poses significant challenges to pedestrian detection in dense and crowded scenes, testing the generalization capabilities of detection algorithms. Our method achieved satisfactory results, effectively demonstrating the robustness and generalization ability of the algorithm in dense environments. As shown in Table 7, the proposed model achieved an MR\({}^{-2}\) of 38.7% on the CrowdHuman dataset, effectively detecting pedestrians in crowded scenes. This performance is attributed to the method's focus on addressing feature loss caused by pedestrian occlusion in crowded environments. By leveraging contextual information to capture potential effective features and incorporating spatial location information of the targets, the detector improves its ability to locate pedestrians. Furthermore, the combination of visual attention and pose information enhances the robustness of visual representations. The proposed model also achieves optimal performance on AP and suboptimal on recall metrics, with experimental results confirming its strong effectiveness. We conducted a comparison of different YOLO versions and RF-DETR on the CrowdHuman dataset, showcasing performance metrics including average precision (AP), MR\({}^{-2}\)%, and FLOPS. As shown in Table 8, Dense-YOLOv8-M (ours) outperformed other models in terms of AP, achieving a score of 92.7, which is significantly higher than YOLOv7 (86.8) and comparable to RF-DETR-B (92.8). Furthermore, Dense-YOLOv8-M maintained a favorable trade-off in terms of MR\({}^{-2}\) at 39.6%, outperforming both YOLOv9-M (42.7%) and YOLOv10-M (42.1%), while offering better efficiency in GFLOPS (82.1). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP** & **Recall** & **MR\({}^{-2}\)** \\ \hline FPN [47] & 83.1 & 90.6 & 52.4 \\ Yolov4 [19] & 75.3 & 92.5 & 64.9 \\ FPN + Soft-NMS [48] & 83.9 & 91.7 & 52.0 \\ FPN + Faster RCNN [47] & 84.5 & 90.2 & 50.4 \\ FPN + AdaptiveNMS [49] & 84.7 & 91.3 & 49.7 \\ RelationNet [50] & 81.6 & - & 48.2 \\ RFB-Net [51] & 78.3 & 94.1 & 65.2 \\ RetinaNet [14] & 80.8 & 93.8 & 63.3 \\ FCOS+AEVB [5] & - & - & 47.7 \\ YOLO-CS [52] & 81.9 & 95.3 & 41.9 \\ NOH-NMS [53] & 89.0 & - & 43.9 \\ Deformable-DETR [54] & 91.5 & - & 43.7 \\ Sparse-RCNN [55] & 91.3 & - & 44.8 \\ Iter-Deformable-DETR [56] & 92.1 & - & 41.5 \\ UniHCP [57] & 92.5 & & 41.6 \\ OTP-NMS [58] & 90.9 & - & 41.2 \\ DDAD [59] & 92.5 & - & 39.7 \\ AMPDet [60] & 88.9 & 97.4 & 44.6 \\ ChainDetection [61] & 91.9 & - & 41.7 \\ PairDETR [62] & 87.2 & & 42.9 \\ NOLA [63] & 89.9 & 96.6 & 45.8 \\ Dense-YOLOv8(ours) & 93.3 & 97.1 & 38.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of miss rate with existing methods on CrowdHuman (AP, recall, and MR\({}^{-2}\)%). Additionally, Dense-YOLOv8-L (ours) achieved an impressive AP of 93.3, which surpasses several advanced models, including YOLOE-v11-L [64], YOLOv10-L (90.1), and YOLOv11-L (91.0). Its MR\({}^{-2}\) was also competitive, at 38.7%, demonstrating effective performance in dense pedestrian detection. Despite having a slightly higher GFLOPS of 169.3 compared to Dense-YOLOv8-M, the model's higher AP and lower MR\({}^{-2}\) underscore its superior detection capability in complex scenarios. These results highlight the effectiveness of the Dense-YOLOv8 models in enhancing pedestrian detection performance while maintaining computational efficiency, especially in dense scenes.