# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 4 Experiment
### Ablation Experiment  
This study enhances the YOLOv7 model through several key modifications, including the design of a lightweight feature extraction module, ELAN-SA, within the Backbone Network, the introduction of a feature fusion module, LGA, in the Head Network, and the replacement of the traditional non-maximum suppression (NMS) algorithm with the CIoUNMS algorithm. To comprehensively assess the effectiveness of these enhancements, we conducted ablation experiments on the VOC2012 pedestrian validation dataset, ensuring that all experimental groups were evaluated under uniform environmental conditions and parameter settings.The findings of these experiments are presented in Table 1.  
The ELAN-SA module markedly enhances feature extraction capabilities, particularly in challenging conditions involving small targets and complex backgrounds. By integrating a shuffle attention mechanism, this module effectively captures fine details within images, resulting in a 2.3% improvement in mAP50 and a 2.6% increase in mAP50:95. Importantly, this enhancement reduces GFLOPs by 2.7 G, indicating an improvement in computational efficiency while maintaining accuracy. Furthermore, the LGA module optimizes multi-scale feature fusion by combining local and global attention mechanisms. This integration enhances the model's expressive power, allowing for better performance in handling targets of varying sizes. The experimental results reveal that the independent introduction of the LGA module led to a 0.6% increase in mAP50 and a 1.5% improvement in mAP50:95. Although there was a slight increase in GFLOPs (0.1 G), the overall enhancement in performance validates the effectiveness of the LGA module. The CIoUNMS algorithm demonstrates a significant advantage in managing overlapping targets, achieving a 1.3% increase in mAP50 compared to traditional NMS. By calculating the CIoU distance between bounding boxes, CIoUNMS effectively retains high-confidence detection boxes, thereby improving recall rates. Additionally, this module enhances the model's convergence speed and stability during training, contributing to the overall performance gains. Figure 7 illustrates the detection performance comparison between YOLOv7 and YOLO-ESL in the same testing scenario. Notably, YOLO-ESL exhibits higher confidence scores for the detected bounding boxes, indicating a higher certainty in target identification. This characteristic underscores the improvements in feature extraction and fusion capabilities of YOLO-ESL, enabling the model to better capture essential features of pedestrians. Specifically, YOLO-ESL not only enhances the accuracy of bounding boxes but also demonstrates greater robustness in complex scenarios characterized by varying lighting conditions and occlusions. This augmented detection capability allows YOLO-ESL to effectively recognize targets in high-density crowds or against intricate backgrounds, thereby reducing the likelihood of false positives and missed detections. Furthermore, a comparison of the detection boxes reveals that YOLO-ESL excels in detecting small targets, a result of the optimized ELAN-SA and LGA modules. Collectively, these findings affirm that the enhancements in YOLO-ESL significantly improve its practicality and accuracy in real-world applications. Ultimately, the YOLO-ESL method achieved a 3.0% improvement in mAP50 and a 2.9% increase in mAP50:95, alongside a reduction of 2.6 G in GFLOPs. These results clearly indicate the substantial contributions of each module's enhancements to the model's performance. Figure 8 presents the detection results of the YOLO-ESL network on the VOC2012 pedestrian test dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **mAP50 (\%)** & **mAP50:95 (\%)** & **GFLOPs** \\ \hline YOLOv7 & 90.7 & 77.2 & 105.1 \\ YOLOv7 + ELAN-SA & 93.0 & 79.8 & 102.4 \\ YOLOv7 + LGA & 91.3 & 78.7 & 105.2 \\ YOLOv7 + CloUNMS & 92.0 & 79.5 & 105.1 \\ YOLOv7 + ELAN-SA + LGA & 93.5 & 80.0 & 102.5 \\ YOLO-ESL & 93.7 & 80.1 & 102.5 \\ \hline \hline \end{tabular}
\end{table}