# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 1 Introduction  
Object detection is one of the main research directions in computer vision, where the main task is to recognize specific classes and precise coordinates of objects in images on demand [1]. With the rapid development of autonomous driving, video surveillance, and other fields, human-centered object detection has received a great deal of attention. In pedestrian detection tasks, crowded and occluded scenarios pose significant challenges. Due to factors such as the angle and distance captured by the detection lens, pedestrian targets vary in scale, and they can overlap with each other, resulting in a limited number of effective feature pixels within the detection area. This makes it difficult to distinguish boundaries, and in such cases, the detector may mistakenly identify multiple pedestrian targets as a single target or experience detection box drift. The challenges mentioned above in crowded pedestrian detection are also commonly encountered in numerous contemporary object detection tasks [2]. Pedestrian detection algorithms can be categorized into two types: traditional manual feature detection and deep learning detection algorithms. Traditional detection algorithms depend on manually designed features for object characterization. These features include Haar wavelet features [3], which combine human movement and appearance. They also encompass HOG features that describe pedestrian contours using edge direction information [4], LBP features with grayscale and rotation invariance [5], and structural SIFT features with scale invariance [5]. Traditional manual feature detection methods have greatly contributed to the development of pedestrian detection research. However, such algorithms exhibit limited robustness in complex scenarios. In recent years, due to therapid development of deep learning, the research focus on pedestrian detection has shifted from manual features to deep learning detection algorithms. The pedestrian detection algorithm based on deep learning, [PERSON] et al. improved Fast R-CNN [6] for pedestrian detection by designing 2 sub-networks for large-scale and small-scale pedestrians [7], and enhanced the efficiency of pedestrian detection by using candidate regions extracted by the ACF detector [8]. [PERSON] et al. analyze the performance of Faster R-CNN [9] in pedestrian detection and use a mixed strategy to classify the candidate regions extracted by RPN through random forest [10]. [PERSON] et al. aim to solve the occlusion issues in crowded pedestrian detection and propose a part-based detection scheme named DeepParts [11]. [PERSON] et al. propose SSPNet [12], which suppresses interference from complex backgrounds using features at different scales and preserves valuable pedestrian information by sharing features. [PERSON] et al. [13] propose a feature-aligned pyramid network to improve the efficiency of dense pedestrian detection by aligning up-sampled features with local features via pixel offsets. The above algorithms have excellent performance in pedestrian detection, but the feature extraction capability for small-scale and occluded pedestrian objects still needs to be enhanced, which is prone to missed and false detection. In order to solve the false and missed detection in dense pedestrian detection, we propose Residual Transformer YOLO (RT-YOLO) based on YOLOv7 [14], and verify its performance through ablation, comparison, and generalization experiments.