基于YOLO模型的作业环境实时反光背心检测

doi:10.12066/j.issn.1007-2861.2686

上海大学学报(自然科学版) ›› 2025, Vol. 31 ›› Issue (4): 691-703.doi: 10.12066/j.issn.1007-2861.2686

基于YOLO模型的作业环境实时反光背心检测

朱硕, 王永芳, 李子萱, 陈伟

上海大学通信与信息工程学院, 上海 200444

收稿日期:2024-12-12 出版日期:2025-08-31 发布日期:2025-09-16
通讯作者: 王永芳(1973—),女,教授,博士,研究方向为计算机视觉和视频图像处理. E-mail:yfw@shu.edu.cn
基金资助:
国家自然科学面上基金资助项目(62275148)

Real-time reflective vest detection in operational environments based on YOLO

ZHU Shuo, WANG Yongfang, LI Zixuan, CHEN Wei

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2024-12-12 Online:2025-08-31 Published:2025-09-16

摘要/Abstract

摘要： 通过引入全局注意力机制(global attention mechanism,GAM),在YOLOv7模型的基础上提出了YOLOv7-GAM模型,增强了对关键区域的关注能力.通过引入多尺度训练方案,提高模型对小目标的感知性能,并设计了一种两阶段增强检测算法,能够有效缓解因遮挡、重叠和小目标问题引起的检测性能下降.在输入图像分辨率为640$\times$640的情况下,该方案的检测速度可满足实际生产环境中的实时性需求,且其性能优于相关的算法.

关键词: 目标检测, 反光背心, 深度学习, YOLO

Abstract: Based on the YOLOv7 model, the YOLOv7-globel attention mechanism (YOLO-GAM) model was proposed to enhance the model's focus on critical regions. Additionally, a multi-scale training scheme was introduced to improve the model's ability to detect small targets, and a two-stage enhanced detection algorithm was designed, which effectively mitigated the degradation of detection performance caused by occlusion, overlapping, and small targets. With an input image resolution of $640\times 640$, the scheme's detection speed could meet the real-time requirements of the actual production environment and outperform the related algorithms in terms of performance.

Key words: target detection, reflective vest, deep learning, YOLO

中图分类号:

TP391.4

朱硕, 王永芳, 李子萱, 陈伟. 基于YOLO模型的作业环境实时反光背心检测[J]. 上海大学学报(自然科学版), 2025, 31(4): 691-703.

ZHU Shuo, WANG Yongfang, LI Zixuan, CHEN Wei. Real-time reflective vest detection in operational environments based on YOLO[J]. Journal of Shanghai University（Natural Science Edition）, 2025, 31(4): 691-703.

参考文献

[1] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features [C]// Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 990517.
[2] Dalal N, Triggs B. Histograms of oriented gradients for human detection [C]// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005: 886-893.
[3] Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.
[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classiflcation with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.
[5] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 580-587.
[6] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[7] Girshick R. Fast R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.
[8] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. Advances in Neural Information Processing Systems, 2015, 39(6): 1137-1149.
[9] Lin T Y, Dollar P, Girshick R Á, et al. Feature pyramid networks for object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 2117- 2125.
[10] Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6154- 6162.
[11] Redmon J, Divvala S, Girshick R, et al. You Only Look Once: unifled, real-time object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 779-788.
[12] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 7464-7475.
[13] Vaswani A. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010
[14] Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers [C]// European Conference on Computer Vision. 2020: 213-229.
[15] Zhao Y, Lu W, Xu S, et al. DETRs beat YOLOs on real-time object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 16965-16974.
[16] Varghese R, Sambath M. YOLOv8: a novel object detection algorithm with enhanced performance and robustness [C]// 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems. 2024: 1-6.
[17] Wang A, Chen H, Liu L, et al. YOLOv10: real-time end-to-end object detection [C]// 38th Conference on Neural Information Processing Systems. 2024: 14458.

基于YOLO模型的作业环境实时反光背心检测

Real-time reflective vest detection in operational environments based on YOLO

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王云亭, 张金艺. 复杂天气条件下道路场景目标检测的域泛化[J]. 上海大学学报(自然科学版), 2025, 31(4): 704-718.
[2]	魏成伟, 周星宏, 李小毛. 基于稳定扩散模型的水下沉船探测[J]. 上海大学学报(自然科学版), 2025, 31(1): 14-27.
[3]	范沈伟, 李国平, 王国中. 基于深度学习的轻量级图像压缩算法[J]. 上海大学学报(自然科学版), 2024, 30(3): 466-475.
[4]	蔡管鸿, 李国平, 王国中, 滕国伟. 基于改进 YOLOv5s 的轻量化交通灯检测算法[J]. 上海大学学报(自然科学版), 2024, 30(1): 94-105.
[5]	张衡, 舒展. 基于点云深度学习的装配式构件尺寸参数识别方法[J]. 上海大学学报(自然科学版), 2023, 29(3): 502-.
[6]	李忠雨, 孙浩东, 李娇. 基于损失加权的实时篮球裁判手势识别系统[J]. 上海大学学报(自然科学版), 2023, 29(1): 68-81.
[7]	杨英杰, 王永芳, 张涵. 基于注意力和反馈机制的 HDR 视频重建[J]. 上海大学学报(自然科学版), 2023, 29(1): 56-67.
[8]	李成范, 赵俊娟. 面向遥感图像的小样本目标检测改进算法研究[J]. 上海大学学报(自然科学版), 2022, 28(2): 314-323.
[9]	王榕榕, 徐树公, 黄剑波. 基于深度学习的图像抠图技术[J]. 上海大学学报(自然科学版), 2022, 28(2): 261-269.
[10]	许庚林, 冉峰, 邓良, 史华康, 郭爱英. 轻量化神经网络和哈希跟踪算法在嵌入式人脸抓拍系统中的应用[J]. 上海大学学报(自然科学版), 2021, 27(6): 1018-1028.
[11]	邢毅雪, 朱永华, 高海燕, 周金, 张克. 基于注意力机制的远程监督实体关系抽取[J]. 上海大学学报(自然科学版), 2021, 27(5): 983-992.
[12]	邵慧翔, 曾丹. 基于改进 YOLOv3 算法的水下小目标分类与识别[J]. 上海大学学报(自然科学版), 2021, 27(3): 481-491.
[13]	陈钰, 丁友东, 于冰, 徐敏. 基于像素流的视频彩色化[J]. 上海大学学报(自然科学版), 2021, 27(1): 18-27.
[14]	陈伟, 杨毅, 李小毛, 刘远, 张鑫. 基于目标候选的 USV 海上船艇检测[J]. 上海大学学报(自然科学版), 2019, 25(5): 668-678.
[15]	胡嘉成, 王向阳, 刘晗. 基于深度学习的连铸坯表面缺陷检测[J]. 上海大学学报(自然科学版), 2019, 25(4): 445-452.