# Pedestrian Detection Based on Feature Enhancement in Complex Scenes
## 1 Introduction  
Target detection mainly accomplishes the function of classifying and localizing objects in scenes. Target detection is the basis of target tracking [1] and intelligent vehicle obstacle avoidance [2]. In addition, target detection has been widely used in areas such as military defense and security surveillance [3; 4; 5]. Pedestrian detection is designed to accurately recognize and detect pedestrians in various scenes [6; 7; 8; 9]. Pedestrian detection technology plays a key role in some areas, such as pedestrian abnormal behavior analysis and intelligent robotics [10]. Pedestrian detection methods include traditional pedestrian detection methods and deep learning pedestrian detection methods. The traditional pedestrian detection method is based on the traditional feature extraction and classifier method in the field of computer vision. Traditional pedestrian detection methods use histogram of oriented gradients (HOG) [11] and Haar-like [12] to extract features such as the local gradient, textures, edges, etc., from the image, thereby describing the shape and appearance of the pedestrian. Classifiers, such as support vector machines, are used to learn the relationship between the features and labels of the samples to classify and discriminate between pedestrians and non-pedestrians. Because traditional methods mainly rely on manually designed features and classifiers, their adaptability to complex scenes, attitude changes and occlusions is poorand they are prone to misdetection and false detection. Meanwhile, traditional methods need to compute and process a large number of features in the training and inference process, which is time-consuming. Deep learning methods for pedestrian detection have made significant progress. Common pedestrian detection methods in deep learning are based on convolutional neural networks. Convolutional neural networks can automatically learn rich features from original images to achieve accurate pedestrian detection. To reduce the computational complexity and speed up the detection process, we commonly use the regional proposal network, which can generate the position and size of the candidate box to complete pedestrian detection. Commonly used pedestrian detection frameworks in deep learning methods include Faster R-CNN [13], you only look once (YOLO) [14], etc. These frameworks can accurately perform the pedestrian detection task. However, deep learning pedestrian detection 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. With the improvement in the detection network, detection performance has been improved to some extent. Despite the progress made, there are still challenges in pedestrian detection, such as low detection accuracy, missed detection, and false detection [15]. To effectively solve the problems of low accuracy, missed detection, and the false detection of pedestrians in different scenes, in this paper the pedestrian detection network PT-YOLO based on the YOLOv5 is proposed, and the detection accuracy of the network is significantly improved by reconfiguring the network. The pedestrian detection network PT-YOLO contains input, a backbone, a neck, and a head. To enhance the feature extraction, the SE module is added to the backbone. Then, the pedestrian detection network PT-YOLO utilizes the BiFPN as the feature fusion module to combine features from different scales. The WIoU loss function is designed to improve the generalization ability of the network and improve the detection accuracy of the network. Finally, the coordconv module is added to the neck to enable the network to accurately localize the pedestrian's position. The main contributions of this paper are as follows:  
1.