收藏切换
Improved YOLO Based on Swin Transformer for Dense Scene Pedestrian Detection Algorithm
收藏切换
PDF
Sen-yi ZHANG, Xue-song ZHANG*, Jia-qi GUO, Hua JIN, Guang-yu LI
Science Technology and Engineering | 2025, 25(21) : 9018 - 9027
Less
收藏切换
Science Technology and Engineering | 2025, 25(21): 9018-9027
Papers·Automation and Computational Technology
Improved YOLO Based on Swin Transformer for Dense Scene Pedestrian Detection Algorithm
Full
Sen-yi ZHANG, Xue-song ZHANG*, Jia-qi GUO, Hua JIN, Guang-yu LI
Affiliations
  • School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian 116052, China
Published: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2406615
Outline
收藏切换

In dense scenes, the frequent occurrence of occluded or small-scale pedestrian objects poses significant challenges to traditional object detection models, frequently leading to a high number of missed detections and false positives. In order to solve the problem of high false negative rate and false positive rate in pedestrian detection in such dense scenes, a novel dense scene pedestrian detection framework called ST-YOLO was proposed. Firstly, the low-level small object detection layer in YOLOv5's backbone network was integrated into the feature pyramid network and path aggregation network structure, adding a pedestrian detection layer for detecting small objects. Secondly, the neck network of YOLOv5 was improved by utilizing multi-scale global information based on Swin Transformer and local information extracted by convolutional neural networks (CNN) to construct aggregated features and enhance the network's feature extraction capability. And the SIoU (scalable IoU) loss function was introduced in the prediction process to accelerate the convergence speed of the model and improve detection capability. Finally, Soft NMS (soft non maximum suppression) was used instead of the original non maximum suppression (NMS) algorithm to reduce the problem of mistakenly deleting detection boxes during the non maximum suppression stage and lower the false alarm rate of the detection algorithm. A large number of experiments on the Wide Person dataset have shown that the improved ST-YOLO algorithm has improved accuracy and mAP0.5 by 5.7% and 3.6% respectively compared to the current mainstream YOLOv9 algorithm.

object detection  /  dense scenes  /  Swin Transformer  /  YOLOv5  /  feature fusion
Sen-yi ZHANG, Xue-song ZHANG, Jia-qi GUO, Hua JIN, Guang-yu LI. Improved YOLO Based on Swin Transformer for Dense Scene Pedestrian Detection Algorithm[J]. Science Technology and Engineering, 2025 , 25 (21) : 9018 -9027 . DOI: 10.12404/j.issn.1671-1815.2406615
Year 2025 volume 25 Issue 21
PDF
235
101
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2406615
  • Receive Date:2024-09-03
  • Online Date:2026-01-13
  • Published:2025-07-28
Article Data
Affiliations
History
  • Received:2024-09-03
  • Revised:2025-04-14
Funding
Affiliations
    School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian 116052, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2406615
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT