# An Improved Lightweight Dense Pedestrian Detection Algorithm
## 1 Introduction  
Pedestrian detection, as an important branch of target detection, has received a lot of attention from researchers due to its great potential in various engineering fields such as visual tracking [1], pedestrian re-identification [2], and behavior recognition [3]. Many target detection algorithms have emerged as a result of the quick development of deep learning. The need for lightweight models is particularly urgent because these algorithms place a focus on improving the accuracy, and they ignore issues with algorithmic model size and detection speed which largely determine how practical the algorithms are and whether they can be used on edge computing devices. Deep-learning-based target detection algorithms can be broadly classified into two categories according to their algorithmic process characteristics: two-stage target detection algorithms and one-stage target detection algorithms. Two-stage target detection algorithms are mainly represented by the region-based convolutional neural networks (R-CNNs) [4; 5; 6] series, which have higher detection accuracy but slower detection speed. One-stage target detection algorithms are represented by the single-shot multi-box detector (SSD) [7] series and the You Only Look Once (YOLO) [8; 9; 10; 11; 12] series, which are reformulated as a regression problem that directly predicts image pixels as objects and their wraparound box properties [13]. These detection algorithms are of mediocre accuracy; however, they are rapid and frequently utilized in industry. YOLO is one of the quickest object detection algorithms available, several orders of magnitude faster than other target detection techniques. YOLO is a target detection technique that differs from region-based algorithms; the network outputs a class probability and an offset value for each bounding box. Thebounding boxes with class probabilities greater than a certain threshold are chosen and utilized to locate targets in the image [14]. Most of the current pedestrian detection tasks are tested and run on high-performance graphics processing units (GPUs), which cannot handle a large number of convolutional computations due to the different computational power of embedded devices and high-performance GPUs. Therefore, a simplified version of the neural network is needed to meet the needs of embedded platforms [15]. [PERSON] [16] pruned the YOLOv4-Tiny convolutional kernel in TRC-YOLO and implemented an extra convolutional layer in the network's residual module to create a ResNet (CSPResNet) structure. [PERSON] [17] presented SAI-YOLO, a lightweight target detection network based on YOLOv4-Tiny with an InceptionV3 structure that replaces two CSPBlock modules with RES-SEBlock modules to lower the number of parameters and computational difficulties. In addition, to replace the original Leaky ReLU function, an enhanced ReLU (M-ReLU) activation function is developed. [PERSON] [18] proposed the DBA module in LMSD-YOLO as a generic lightweight convolution unit to build the entire lightweight model; second, the improved S-MobileNet module is designed as the backbone feature extraction network, which can be used to enhance feature extraction without adding additional computation; then, the DSAFF module is proposed to achieve the adaptive fusion of multi-scale features with fewer parameters; and, finally, SIoU is used as a loss function to accelerate model convergence and improve detection accuracy. [PERSON] [19] suggested a portable target detection approach based on the MobileNetv2 and YOLOv4 algorithms. To decrease the number of model parameters and the size of the model, MobileNetv2 and depth-separable convolution are coupled. Due to the varying processing power of embedded devices and large servers, many edge computing devices and mobile terminals do not have the requisite computational capability for modern state-of-the-art networks in practical applications. The accuracy of pedestrian recognition is further reduced in pedestrian-dense environments due to several target frames overlapping each other.