# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 5 Conclusions  
This paper introduces the RT-YOLO algorithm, aimed at enhancing the detection accuracy of small-scale and obscured objects in crowded pedestrian scenarios. The powerful BOTrans module combines convolutional network with Transformer structure, replacing the E-ELAN structure in the original network. BOTrans globally extracts features and integrates contextual information to distinguish crowded and occluded objects. Based on the above enhancements, the multi-scale fusion strategy changes to design special network layers for small-scale and occluded objects, allowing them to obtain feature maps with different scale receptive fields. This design ensures that the feature maps have semantic information from high-level features while retaining detailed information from low-level features. In RT-YOLO, the NAM attention mechanism is introduced, allowing the network to focus on the object region of interest efficiently, thereby improving network parameter utilization. Finally, BTDetect decouples the maximum scale feature map to obtain classification and location information for objects, resulting in precise object localization.  
In the filtered CrowdHuman and WiderPerson datasets, experimental results demonstrate that RT-YOLO improves \(mAP_{50}\) by 3.8% and 3.4%, \(mAP_{5095}\) by 5.1% and 4% when compared to YOLOv7. Generalization experiments on Pascal VOC2007 validate its robustness and effectiveness, showcasing a 5.1% improvement in mAP over YOLOv7. Although the addition of extra modules slightly increases algorithm speed and complexity, RT-YOLO still meets real-time requirements. From the experimental results, it can be seen that there is room for improvement in RT-YOLO in terms of lightweighting and loss functions. For example, using dilated convolutions instead of regular convolutions can reduce model complexity, although it may lead to a performance drop. Adjusting loss function allocation strategies with Focal Loss or choosing to pair GIOU and CIOU loss functions based on the object type. Subsequent work will continue to achieve better algorithmic performance.  
Conceptualization, H.Y. and Y.W.; methodology, H.Y.; software, H.Y.; validation, H.Y. and Y.W.; writing--review and editing, H.Y. and Y.W.; visualization, H.Y.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Natural Science Foundation of Shaanxi Province, China (Nos. 2020 JM-499 and 2020 JQ-684) and the National Natural Science Foundation of China (No. 61803294). Not applicable. The source of all experimental data in the manuscript is open-access datasets publicly available for pedestrian-related research, with corresponding references to the datasets: [[https://arxiv.org/abs/1805.00123](https://arxiv.org/abs/1805.00123)]([https://arxiv.org/abs/1805.00123](https://arxiv.org/abs/1805.00123)) and [[https://paperswithcode.com/dataset/widerperson](https://paperswithcode.com/dataset/widerperson)]([https://paperswithcode.com/dataset/widerperson](https://paperswithcode.com/dataset/widerperson)), accessed on 25 October 2023. Data Availability Statement: Data available on request from the authors. The authors declare no conflict of interest.