# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 4 Experiments  
### Experimental Environment Configuration and Data Introduction  
Due to the limited availability of publicly available datasets for rainy weather conditions, we propose the RainDet3000 dataset, based on the Cityscapes' [29] annotation protocol, which aims to provide a more accurate representation of real-world rainy scenarios. The dataset consists of 3000 images, including 2100 for training, 600 for testing, and 300 for validation. The images in RainDet3000 are collected from both camera shots and the Internet and include two object classes: vehicles and pedestrians. The dataset is annotated using the Labelling tool, ensuring high-quality and consistent labeling across all images. A portion of the dataset is shown in Figure 10.  
In addition to a subset of images from the RainDet3000 dataset, we also utilize publicly available datasets, including UA-DETRAC [30], Caltech Pedestrian Dataset [31], and KITTI [32], as the training set for our proposed method. By incorporating images from multiple datasets, our model can learn from a wide range of diverse scenarios, improving its robustness and generalization ability for pedestrian detection in various conditions. The BBD100K [33] dataset is a large-scale benchmark dataset that includes a diverse range of driving images captured under various conditions. The dataset features images captured at different times of the day, including early morning, midday, evening, and night; this provides a comprehensive evaluation of object detection algorithms under different lighting conditions. Additionally, the dataset includes a wide range of challenging weather scenarios, such as rainy, cloudy, and snowy days, which further enhances the complexity  
Figure 10: Partial RainDet3000 dataset. and diversity of the dataset. In order to further validate the robustness of our proposed pedestrian detection network, we conduct experiments on a subset of the BDDI00K dataset that exclusively contains images of pedestrian targets. This subset, which we refer to as BBD100K-pedestrian, comprises a total of 8823 images that have been selected based on rigorous screening criteria. In this dataset, 6176 images are used as the training set, 1765 images are used as the test set, and 882 images are used as the validation set. For our experimentation, we conduct the experiments on a notebook computer equipped with an Intel i5 10200H processor, NVIDIA GeForce RTX 2060 graphics, and 8 GB of memory. We employ widely used deep learning frameworks and image processing libraries commonly applied in object detection tasks. The experimental hardware and parameter settings are summarized in Table 1.  
In this experimentation, the initial network input size is fixed at 640 \(\times\) 640. A momentum of 0.9 is utilized, along with a batch size of 4 per epoch. The learning rate is set to 0.001 and a weight decay of 0.05 is implemented to ensure experimental fairness and consistency. All comparative experiments adhere to these identical parameter settings. Figure 11 shows the loss curve of network training. ### Experimental Environment Configuration and Data Introduction  
To assess the performance of the network, the study uses mean average precision (mAP) as the detection accuracy index. In addition, the study also employs frames per second (FPS) as the evaluation index for the detection speed and the model reduction ratio R as the evaluation index for the level of model lightweighting. These metrics provide a comprehensive evaluation of the network's performance, taking into account both accuracy and efficiency. By utilizing these metrics, the study is able to provide a comprehensive and accurate analysis of the network's capabilities, taking into account both accuracy and efficiency and enabling a more informed assessment of its potential for practical applications.