# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 4 Experiment  
### Datasets  
In this experiment, we selected the VOC2012 dataset [30], also known as the Visual Object Classes Challenge 2012, as the benchmark for evaluating the YOLO-ESL network. This dataset is widely used in the field of computer vision for image recognition, classification, and detection tasks, making it particularly suitable for our research.  
VOC2012 is a standard dataset in the object detection domain, encompassing multiple object categories (such as people, animals, and vehicles) and precise bounding box annotations, thereby supporting our training and evaluation for the pedestrian detection task. This dataset features a diverse range of scenes and complex backgrounds, including varying lighting conditions, occlusions, and target densities, which are particularly relevant for validating the robustness and adaptability of YOLO-ESL in complex environments.  
In our experiments, we filtered the images and annotations specific to the \"person\" category, totaling 9583 images, which were then divided into training, validation, and test sets. The training set comprised 8633 images, while both the validation and test sets contained 475 images each. This distribution was determined through an analysis of the overall dataset distribution, ensuring that the training set's sample size was sufficient to enhance the model's generalization ability and robustness. The sample sizes for the validation and test sets were also carefully considered to effectively tune hyperparameters during model training and provide reliable performance evaluations.  
Although the VOC2012 dataset includes images of other categories, we chose not to evaluate these categories because the YOLO-ESL algorithm is specifically designed to enhance the accuracy and robustness of pedestrian detection. Focusing on the \"person\" category allowed for a more concentrated and in-depth assessment of the results. Additionally, pedestrian detection poses various challenges in practical applications, such as small object detection, occlusion, and complex backgrounds; thus, concentrating on the \"person\" category effectively tested the performance of YOLO-ESL under these conditions. The selected 9583 images of the \"person\" category provided an adequate sample for training and evaluation, ensuring the validity of the experiments.  
To ensure a balanced representation across these datasets, we employed a random sampling method to guarantee that samples of the \"person\" category were representative in each dataset, avoiding an over-concentration of any single category. Moreover, we ensured that the validation and test sets did not overlap in samples to provide an objective standard for performance evaluation. Through these strategies, we established a reasonable balance among the training, validation, and test sets, providing solid support for the performance assessment of YOLO-ESL.  
In conclusion, the standardization, rich annotation information, and diverse scene characteristics of the VOC2012 dataset made it an ideal choice for evaluating the performance of the YOLO-ESL network, thereby providing a solid foundation for our research.