# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 1 Introduction  
Pedestrian detection is one of the hot research directions in the field of computer vision; it refers to the processing of images or videos by computers to automatically filter out pedestrian targets among them. Usually, traditional pedestrian detection algorithms consist of three steps: image preprocessing, manual feature extraction, and classification. The purpose of image preprocessing is to extract more effective features. The manual extraction process is generally detected by a sliding window approach, which uses a rectangular box to scan comprehensively from the top left to the bottom right of the image and obtains one or more specific features (e.g., edge features, image blocks, trait features, wavelet coefficients, etc.) from the input image. Some of the representative features are LBP [1], HOG [2], Edgelet [3], Shapelet [4] and Haar-like [5], Gabor, and other features. Traditional pedestrian detection has advantages such as fast detection speed, but the feature information is single, which leads to the low recognition accuracy of the network. With the development of deep learning, a variety of excellent detection algorithms have emerged that effectively solve the problem of the low recognition rate of traditional pedestrian detection. The mainstream of target detection algorithms are divided into twocategories: two-stage target detection algorithms represented by RCNN [6], Fast RCNN [7], and Faster RCNN [8] and single-stage target detection algorithms represented by  
YOLO [9; 10] and FCOS [11]. The former obtains candidate regions with targets first and then performs regression prediction on the size and position of the obtained candidate frames; the latter generates several candidate frames and performs regression prediction. Single-stage target detection is fast but less accurate; two-stage target detection is more accurate but slower. There are challenges in pedestrian detection tasks, such as complex weather conditions and severe occlusions. In addition, existing object detection networks suffer from issues such as large model parameters and slow inference speed. To address these problems, many researchers have proposed solutions specifically targeting these issues. For instance, some have suggested replacing CSPDarknet53 with the lightweight MobileNet [12] to reduce the network parameters. [PERSON] et al. [13] proposed the CA-MobileNetv2-YOLOv4 network, which replaces the CSPDarknet53 backbone network of YOLOv4 with MobileNetv2 and adds a coordinate attention mechanism to adjust the weights and focus the network on the regions of interest. The algorithm proposed by [PERSON] et al. [14] takes YOLOv4-Tiny as the baseline and incorporates the Ghost [15] module and dilated convolution. This reduces the model's capacity significantly. However, it limits the model's ability to learn advanced features and the authors did not consider that dilated convolution might be more effective for larger objects and less friendly for smaller objects. The pedestrian detection algorithm based on YOLOv4 proposed by [PERSON] et al. [16] uses ShuffleNet instead of YOLOv4 and uses depth-separable convolution to reduce the model size. While the network becomes more lightweight, its feature extraction capability decreases. In complex detection scenarios, this may lead to a significant decrease in recall rate. [PERSON], [PERSON] [17] was able to improve the detection performance of the network in cases of obstacle occlusion by using YOLOv4-Tiny as the baseline, incorporating multispectral methods and reducing latency. The \(\alpha\)-CIoU loss function is used in [PERSON], [PERSON] [18]'s proposed YOLOv5s-G2 network to improve the problem of occlusion and small target unrecognition in the pedestrian detection task. The mentioned GhostC3 module in the paper increases the structural complexity of the network, which could potentially result in less than optimal model inference speed.