[1]院老虎,常玉坤,刘家夫.基于改进YOLOv5s的雾天场景车辆检测方法[J].郑州大学学报(工学版),2023,44(03):37-43.[doi:10.13705/j.issn.1671-6833.2023.03.005]
　YUAN Laohu,CHANG Yukun,LIU Jiafu.Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):37-43.[doi:10.13705/j.issn.1671-6833.2023.03.005]
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基于改进YOLOv5s的雾天场景车辆检测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年03期
页码:
37-43
栏目:
出版日期:
2023-04-30

文章信息/Info

Title:
Vehicle Detection Method Based on Improved YOLOv5s in Foggy Scene
作者:
院老虎; 常玉坤; 刘家夫
沈阳航空航天大学 航空宇航学院,辽宁 沈阳 110136

Author(s):
YUAN Laohu; CHANG Yukun; LIU Jiafu
Shenyang University of Aeronautics and Astronautics University of Aeronautics and Astronautics, Liaoning Shenyang 110136

关键词:
深度学习;  YOLOv5s;  车辆检测;  数据增强;  雾气模拟
Keywords:
deep learning;  YOLOv5s;  vehicle detection;  data augmentation;  fog simulation
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2023.03.005
文献标志码:
A
摘要:
为了解决现有的目标检测方法在雾天场景下存在识别准确率低、易漏检的问题，提出一种改进 YOLOv5s 的雾天车辆检测方法。首先，以 VisDrone 数据集为基础，通过大气散射模型生成轻雾数据集( LightFogVisDrone) 和浓雾数据集( ThickFogVisDrone) ，并收集真实雾天场景图片组成混合浓度数据集( MixFogData) ; 其 次，对 原 始 YOLOv5s 的 Mosaic 数据增强方式进行改进，由原始 的 4 张 图 片 改 为 9 张图片进行随机剪切，减 少 灰 色 背 景面积，加快模型收敛，提高训练效率，在预测端之前添加 CBAM 注 意 力 机 制，以此来增强模型的图像特 征提取能力，改善遮挡目标与小目标的漏检问题; 最 后，优 化 NMS 非极大抑制值先验框，改善车辆目标的 漏检问题。实验结果表明: 与原 始 YOLOv5s 相 比，改 进 YOLOv5s 在 轻 雾、浓雾和混合雾气状态下的平均 精确率分别提高了 16. 14、16. 16 和 15. 05 百分点。改进 YOLOv5s 对于雾天环境下车辆目标的检测具有 有效性和实用性。
Abstract:
In order to solve the problems of low recognition accuracy and easy omission of existing target detection methods in foggy scenes, an improved vehicle detection method based on YOLOv5s was proposed. Firstly, based on VisDrone data set, LightFogVisDrone and ThickFogVisDrone were generated by atmospheric scattering model,and the MixFogData was composed of real fog scene pictures. Secondly, the Mosaic data enhancement method of the original model was improved from the original 4 pictures to 9 pictures randomly, which reduced the gray background area, accelerated the convergence of the model and improved the training efficiency, and the CBAM attention mechanism module was added before the prediction end to improve the feature extraction ability of the network to tackle the problem of missed detection of occluded targets and small targets. Finally, the prior frame of NMS non-maximum suppression value was optimized to improve the problem of missing detection of vehicle targets. The experimental results showed that, compared with the original YOLOv5s, the average accuracy of the improved YOLOv5s in light fog, dense fog and mixed fog was increased by 16.14, 16.16 and 15.05 percentage points, respectively, which proved that the improved YOLOv5s was effective and practical for vehicle target detection in foggy environment.

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更新日期/Last Update: 2023-05-08