收藏切换
Research on Pedestrian Detection Based on DETR
收藏切换
Ping Zhang, Cuinan Yue, Yibin He, Yanlong Wang
Automobile Technology | 2026, (5) : 1 - 6
Less
收藏切换
Automobile Technology | 2026, (5): 1-6
• Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles •
Research on Pedestrian Detection Based on DETR
Full
Ping Zhang, Cuinan Yue, Yibin He, Yanlong Wang
Affiliations
  • School of Automobile, Chang'an University, Xi'an 710018
Published: 2026-05-24 doi: 10.19620/j.cnki.1000-3703.20250527
Outline
收藏切换

To address the issue of low detection accuracy for small pedestrian targets, a pedestrian detection model tailored for complex scenes is proposed based on the DETR algorithm. In the feature extraction stage, the SENet attention mechanism is introduced to enhance channel discrimination ability. By combining image Relative Position Encoding (iRPE) with absolute position encoding, the model's perception of global context and local details is improved. After training on a self-built dataset, experimental results show that the pedestrian recognition accuracy of the proposed scheme reaches 97.2%, with false detection rate and missed detection rate of only 1.7% and 1.1%, respectively. It performs excellently in handling complex scenes such as multi-scale pedestrians, partial occlusion, and low visibility, and its detection performance is significantly better than traditional methods.

Intelligent driving  /  Pedestrian detection  /  Transformer  /  DETR  /  Pedestrian safety
张平, 岳粹楠, 贺艺斌, 王龑龙. 基于改进DETR算法的行人检测研究. 汽车技术, 2026 , (5) : 1 -6 . DOI: 10.19620/j.cnki.1000-3703.20250527
Ping Zhang, Cuinan Yue, Yibin He, Yanlong Wang. Research on Pedestrian Detection Based on DETR[J]. Automobile Technology, 2026 , (5) : 1 -6 . DOI: 10.19620/j.cnki.1000-3703.20250527
Year 2026 volume Issue 5
137
26
Cite this Article
BibTeX
Article Info
doi: 10.19620/j.cnki.1000-3703.20250527
  • Online Date:2026-09-23
  • Published:2026-05-24
Article Data
Affiliations
History
  • Revised:2025-10-29
Affiliations
    School of Automobile, Chang'an University, Xi'an 710018
References
Share
https://castjournals.cast.org.cn/joweb/qcjs/EN/10.19620/j.cnki.1000-3703.20250527
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
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
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