[1]裴玉龙,丛薇.基于改进YOLOv11-Pose的行人不良过街姿态识别方法[J].长安大学学报(自然科学版),2026,46(2):117-127.[doi:10.19721/j.cnki.1671-8879.2026.02.009]
　PEI Yu-long,CONG Wei.Improved YOLOv11-Pose-based recognition method for pedestrian improper crossing posture[J].Journal of Chang’an University (Natural Science Edition),2026,46(2):117-127.[doi:10.19721/j.cnki.1671-8879.2026.02.009]
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基于改进YOLOv11-Pose的行人不良过街姿态识别方法()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第46卷
期数:
2026年2期
页码:
117-127
栏目:
交通工程
出版日期:
2026-04-18

文章信息/Info

Title:
Improved YOLOv11-Pose-based recognition method for pedestrian improper crossing posture
文章编号:
1671-8879(2026)02-0117-11
作者:
裴玉龙; 丛薇
(东北林业大学 土木与交通学院,黑龙江 哈尔滨 150040)
Author(s):
PEI Yu-long;  CONG Wei
(School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)
关键词:
交通工程;  姿态识别;  YOLOv11-Pose;  不良过街;  WIoU;  自校准模块
Keywords:
traffic engineering;  posture recognition;  YOLOV11-Pose;  improper crossing;  WIoU;  self-calibration module
分类号:
U491.1
DOI:
10.19721/j.cnki.1671-8879.2026.02.009
文献标志码:
A
摘要:
针对复杂交通场景中行人不良过街姿态难以精准识别的问题,提出一种基于改进YOLOv11-Pose模型的行人不良过街姿态识别算法,实现对危险过街姿态的准确检测。围绕模型结构与损失函数进行系统优化,首先引入WIoU损失函数替代原有边界框回归损失,通过动态权重调节机制增强难样本学习能力,提升定位精度并加快模型收敛; 其次在主干网络中嵌入SBAM注意力机制,构建空间与通道协同增强结构,提高对远距离、小尺度行人的特征表达能力; 进一步将自校准模块与C3k2模块进行融合,强化特征重标定能力与边界区域细节感知能力,从而提升复杂背景下关键点检测的稳定性与姿态判别的准确性。模型以人体关键点信息为基础构建姿态结构特征映射,实现对不良过街姿态的精细识别。研究结果表明:在自建行人不良过街姿态数据集上,改进模型平均精度均值较原模型提升了4.2%,精确率提升了3.4%,召回率提升了6.7%,在小目标与复杂场景子集中漏检率明显下降,模型收敛轮次减少,训练过程更加稳定; 该方法在检测精度、鲁棒性与泛化能力方面均取得显著提升,可作为智能辅助驾驶与公共安全监控系统中的关键感知模块,为危险过街行为的实时识别与主动预警提供支撑,对于降低交通事故发生率、提升道路通行安全水平与运行效率具有参考价值。
Abstract:
Aiming at the problem that pedestrian improper crossing postures are difficult to accurately recognize in complex traffic scenes, a pedestrian improper crossing posture recognition algorithm based on the improved YOLOv11-Pose model was proposed to achieve accurate detection of dangerous crossing postures. Systematic optimization was conducted on the model architecture and loss function. First, the WIoU loss function was introduced to replace the original bounding box regression loss, and a dynamic weight adjustment mechanism was adopted to enhance the learning capability for hard samples, thereby improving localization accuracy and accelerating model convergence. Second, the SBAM attention mechanism was embedded into the backbone network, constructing a spatial-channel collaborative enhancement structure to improve the feature representation capability for long-distance and small-scale pedestrians. Furthermore, the self-calibration module was integrated with the C3k2 module, strengthening feature re-calibration and boundary-region detail perception, which improved the stability of keypoint detection and the accuracy of improper crossing posture discrimination under complex backgrounds. Based on human keypoint information, the model constructs posture structural feature mappings to achieve fine-grained recognition of improper crossing postures. Experimental results on a self-built pedestrian improper crossing posture dataset show that the improved model increases mAP@0.5 by 4.2%, precision by 3.4%, and recall by 6.7% compared with the original model. In the small-target and complex-scene subsets, the miss detection rate decreases significantly, the number of convergence epochs is reduced, and the training process becomes more stable. The proposed method demonstrates significant improvements in detection accuracy, robustness, and generalization capability. It can serve as a key perception module in intelligent driving assistance and public safety monitoring systems, providing support for real-time recognition and active warning of dangerous road-crossing behaviors. It has practical value for reducing traffic accident rates and improving road safety and traffic operation efficiency.5 tabs, 6 figs, 33 refs.

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备注/Memo

备注/Memo:
收稿日期:2025-08-05
基金项目:国家自然科学基金项目(71771047); 中央高校基本科研业务费专项资金项目(2572023CT21-02)
作者简介:裴玉龙(1961-),男,黑龙江桦川人,教授,工学博士,从事交通规划与设计研究,E-mail:peiyulong@nefu.edu.cn。
更新日期/Last Update: 2026-04-20