[1]马留洋,胡争争,栗武华.基于 AR-SSVEP 和 YOLOv3 的时敏目标识别方法[J].郑州大学学报(工学版),2025,46(04):32-39.[doi:10.13705/j.issn.1671-6833.2025.01.017]
　MA Liuyang,HU Zhengzheng,LI Wuhua.Time-sensitive Target Recognition Method Based on AR-SSVEP and YOLOv3[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):32-39.[doi:10.13705/j.issn.1671-6833.2025.01.017]
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基于 AR-SSVEP 和 YOLOv3 的时敏目标识别方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年04期
页码:
32-39
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
Time-sensitive Target Recognition Method Based on AR-SSVEP and YOLOv3
文章编号:
1671-6833(2025)04-0032-08
作者:
马留洋;  胡争争;  栗武华
中国电子科技集团公司第二十七研究所,河南 郑州 450047
Author(s):
MA Liuyang;  HU Zhengzheng;  LI Wuhua
The 27th Research Institute of China Electronics Technology Group Corporation, Zhengzhou 450047, China)
关键词:
增强现实;  人工智能;  时敏目标;  目标检测;  稳态视觉诱发电位;  目标识别
Keywords:
augmented reality;  artificial intelligence;  time-sensitive target;  object detection;  steady state visual evoked potential;  target recognition
分类号:
R318. 04TP391. 4
DOI:
10.13705/j.issn.1671-6833.2025.01.017
文献标志码:
A
摘要:
针对目标跟踪过程中目标身份 ( ID) 跳变而影响时敏目标识别的问题,提出了一种融合增强现实技术(AR) 、稳 态 视 觉 诱 发 电 位 ( SSVEP ) 和 YOLOv3 的 人 在 回 路 的 “ 检 测-决 策” 时 敏 目 标 识 别 方 法 ( AR-SSVEPYOLOv3) 。 利用目标感知模块获取前端场景视频,并通过 AR 眼镜实时呈现,YOLOv3 算法完成场景视频中敏感目标检测,AR-SSVEP 脑电处理模块解析受试者的脑电数据,在 ID 变化过程中对时敏目标进行识别。 对比分析时敏目标的识别率,结果表明:AR-SSVEP-YOLOv3 时敏目标识别方法相比 YOLOv3 算法识别率平均提升了 18. 8%,相比 YOLOv3-Sort 算法平均提升了 8. 0%。 AR-SSVEP-YOLOv3 时敏目标识别方法可以降低目标 ID 跳变对时敏目标识别的影响,提升人机交互能力和时敏目标识别率。
Abstract:
To address the problem of target identity ( ID) fluctuation during target tracking, which might affect the time-sensitive target recognition, an " detection-decision" time-sensitive target recognition method ( AR-SSVEP-YOLOv3) was proposed which integrated augmented reality (AR) technology, steady state visual evoked potential ( SSVEP) , and YOLOv3. The target perception module obtained the front-end scene video and presented it in reatime through an AR headset. The YOLOv3 algorithm completed the detection of sensitive targets in the scene video, and the AR-SSVEP EEG processing module decoded the EEG data of the subject during ID changes to identify time-sensitive targets. The correct recognition rate of time-sensitive targets was compared and analyzed. The results showed that the average improvement was 18. 8% in the recognition accuracy of AR-SSVEP-YOLOv3 time-sensitive target recognition method compared with the YOLOv3 algorithm, and the average improvement was 8. 0% compared with the YOLOv3-Sort algorithm. The AR-SSVEP-YOLOv3 time-sensitive target recognition method could reduce the influence of target ID fluctuation on time-sensitive target recognition and improve the human-computer interaction ability and the correct recognition rate of time-sensitive targets.

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更新日期/Last Update: 2025-07-13