# Pedestrian Detection Based on Feature Enhancement in Complex Scenes
## 2 Related Work
### Deep Learning Target Detection Method  
Deep learning target detection methods are broadly divided into two categories. One classification is the single-stage algorithm, which directly takes the image as the input and produces the corresponding bounding box for the category. This approach is exemplified by the YOLO algorithm introduced by [PERSON] et al. in 2016. The YOLO network is a collective term for a large target detection network. The YOLO network includes different detection models, such as YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv7, etc., instead of presenting each the YOLO network individually. Another classification is the two-stage algorithm, where the algorithm first generates region proposals and then utilizes convolutional neural networks for classification and localization to obtain bounding boxes. Representative examples of this category include a faster region-based convolutional neural network (Faster R-CNN) and a fast region-based convolutional neural network (Fast R-CNN) [24]. [PERSON] et al. proposed the YOLO network. The network takes the whole image as an input to the network, and the output is the category and position of the regression target box. The detection speed is significantly improved. [PERSON] et al. [25] proposed an improved object detection method based on the idea of feature fusion, which improves the detection accuracy and detection speed of the network by fusing the outputs of different layers. [PERSON] et al. [26] introduced the convolutional block attention module (CBAM), which enhanced both the channel and spatial information; thereby, the CBAM module can further improve the feature extraction capability of the network. [PERSON] et al. [27] proposed the squeeze-and-excitation (SE) module, which enables the network to focus on important features while suppressing unnecessary ones. [PERSON] et al. [28] introduced the Swin-Transformer module, which utilizes different down sampling feature maps at different stages to output multi-scale feature information. [PERSON] et al. [29] reconstructed the backbone network of the YOLOv5 network using Res2 Block to improve the fine-grained feature fusion capability of the network. [PERSON] et al. [30] proposed a pedestrian detection method based on a small sample dataset. The mosaic data enhancement method was used to increase the number of datasets. This method improves the comprehensive performance of the model withoutchanging the number of existing data sets and networks, and its detection accuracy is 91.97%. [PERSON] and [PERSON] [31] designed a multi-scale feature fusion pedestrian detection network based on a transformer. In the detection phase, the transformer can capture global information, effectively solve the long-distance dependency mechanism between image pixels, and improve the pedestrian detection effect. The detection accuracy is 78.5%. In previous research, pedestrian detection mainly includes traditional detection methods and deep learning detection methods. Traditional detection methods are relatively simple to train and use. However, because traditional detection methods need to perform redundant traversal operations on images, the time needed to use them and their complexity are high. Deep learning detection methods have a simple structure, high efficiency and high precision. However, deep learning methods require a large amount of data for training and also require the computer to have good computational performance. A large number of studies show that the detection accuracy of deep learning pedestrian detection methods is better than that of traditional pedestrian detection methods. Therefore, deep learning pedestrian detection methods are mainstream detection methods at present. However, because pedestrians on the scene will be affected by factors such as occlusion, scale transformation, lighting changes, and posture changes, the detection results have problems such as low accuracy, missed detection, and false detection.