# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 5 Conclusions  
This paper presents the YOLO-ESL algorithm, an enhancement of YOLOv7, specifically addressing the challenges of pedestrian detection accuracy. The traditional YOLOv7 algorithm employs the ELAN module within its backbone network; however, it suffers from limited flexibility and low computational efficiency. To overcome these limitations, we introduce the lightweight feature extraction module ELAN-SA, which significantly reduces the model's computational and parameter demands while enhancing detection efficiency. Additionally, we design a new feature fusion module, LGA, which effectively integrates key semantic information from features at different scales, further improving model performance. To optimize the detection result filtering process, we replace the traditional NMS algorithm with CIoUNMS. Extensive evaluations of the enhanced network architecture on the VOC2012 pedestrian dataset reveal that YOLO-ESL demonstrates significant improvements in both mAP50 and mAP50:95, underscoring its effectiveness and advantages in pedestrian detection applications. Nevertheless, despite its strong performance, YOLO-ESL may still encounter issues such as missed detections and false positives under specific challenging conditions. In low-light scenarios, for instance, the model may overlook pedestrians positioned far from light sources. Similarly, in occluded situations, partially obstructed pedestrians might not be accurately identified, and background objects could be misclassified as pedestrians. Furthermore, the model may struggle to effectively detect exceedingly small targets, a challenge particularly pronounced in crowded environments. Analyzing the types and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mAP50 (\%)** & **mAP50:95 (\%)** & **GFLOPs** & **FPS** \\ \hline SSD & 78.7 & 45.7 & 60.9 & 59 \\ YOLOv5 & 80.5 & 62.7 & 108.2 & 175 \\ YOLOx & 90.9 & 88.6 & 155.67 & 185 \\ YOLOv7 & 90.7 & 77.2 & 105.1 & 208 \\ YOLOv8 & 90.5 & 78.9 & 165.4 & 210 \\ YOLO-ESL (ours) & 93.7 & 80.1 & 102.5 & 217 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experiment results. frequencies of these errors can provide valuable insights into the model's limitations and guide future improvements. In addition, the application of the YOLO-ESL model to other object detection tasks, such as vehicle detection, shows promising potential, highlighting its broad applicability and scalability. The improvements within YOLO-ESL, particularly the introduction of the shuffle attention mechanism and enhanced feature extraction capabilities, are expected to be further expanded in future versions of YOLO, such as YOLOv9 [32]. These advancements not only improve detection accuracy in complex environments but also optimize the model's performance across a wider range of object detection tasks. While predicting the performance of these future versions poses challenges, rigorous experimental validation will allow us to better assess the potential applications of the YOLO-ESL algorithm within these new models. We hope that this study offers valuable insights for ongoing research in the YOLO series and fosters continuous innovation and progress in object detection technologies. Conceptualization, F.W. ; methodology, F.W. ; software, F.W. ; validation, F.W., J.W. and X.Y. ; formal analysis, F.W. ; investigation, F.W. ; resources, F.W. ; data curation, F.W. ; writing--original draft preparation, F.W. ; writing--review and editing, F.W., J.W. and X.Y. ; visualization, J.W. ; supervision, X.Y. ; project administration, X.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.