[1]汤林东,云利军,罗瑞林,等.基于改进 YOLOv5s 的复杂道路交通目标检测算法[J].郑州大学学报(工学版),2024,45(03):64-71.[doi:10. 13705 / j. issn. 1671-6833. 2024. 03. 016]
　TANG Lindong,YUN Lijun,LUO Ruilin,et al.Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):64-71.[doi:10. 13705 / j. issn. 1671-6833. 2024. 03. 016]
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基于改进 YOLOv5s 的复杂道路交通目标检测算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年03期
页码:
64-71
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s
文章编号:
1671-6833(2024)03-0064-08
作者:
汤林东1; 2 ;  云利军1; 2 ;  罗瑞林3 ;  卢 琳3
1. 云南师范大学 信息学院,云南 昆明 650500;2. 云南师范大学 云南省教育厅计算机视觉与智能控制技术工程研 究中心,云南 昆明 650500;3. 云南省烟草烟叶公司,云南 昆明 650500
Author(s):
TANG Lindong 1; 2; YUN Lijun 1; 2; LUO Ruilin 3; LU Lin 3
1. College of Information, Yunnan Normal University, Kunming 650500, China; 2. Yunnan Provincial Department of Education Computer Vision and Intelligent Control Technology Engineering Research Center, Yunnan Normal University, Kunming 650500, China; 3. Yunnan Tobacco Leaf Company, Kunming 650500, China
关键词:
自动驾驶;  目标检测;  YOLOv5s;  MHSARM;  CoordConv
Keywords:
automatic driving;  target detection;  YOLOv5s;  MHSARM;  CoordConv
分类号:
TP391
DOI:
10. 13705 / j. issn. 1671-6833. 2024. 03. 016
文献标志码:
A
摘要:
针对目前自动驾驶场景下交通目标检测算法抗复杂背景干扰能力弱,导致检测性能不足的问题,提出了 一种改进 YOLOv5s 的复杂道路交通目标检测算法。 首先,在特征提取区域,采用多头自注意残差模块( MHSARM) 来强化待检目标特征信息,弱化复杂背景干扰;其次,在特征融合区域,采用 CoordConv 代替传统 Conv,使网络具备 空间信息感知能力,提升网络检测精度。 在开源数据集 Kitti 及 BDD100K 上的实验结果表明:改进 YOLOv5s 算法 在复杂道路中具备更强的特征提取能力及良好的泛化能力,mAP_0. 5 分别达到 93. 3% 和 47. 4%,与 YOLOv5s 相 比,分别提升了 0. 9%和 1. 4%。 另外,改进 YOLOv5s 相较于目前最新的目标检测算法 YOLOv7、YOLOv8,mAP_0. 5 分别提高了 1. 3%和 2. 2%,与在 Kitti 数据集上最新的研究成果 Sim-YOLOv4 算法相比,mAP_0. 5 提高了 2. 2%。
Abstract:
A complex road traffic object detection algorithm was proposed to address the issue of traffic target detection algorithms′ inability to resist complex background interference and insufficient detection performance in the current autonomous driving scenario. At first, the multi-head self-attention residual module (MHSARM) was used to improve the feature information of the target to be inspected while decreasing the complex background interference. Secondly, in the feature fusion area, CoordConv was used instead of traditional Conv, so that the network could perceive spatial information and improve network detection accuracy. The improved YOLOv5s algorithm had stronger feature extraction ability and good generalisation ability in complex roads, and mAP_0.5 reached 93.3% and 47.4%, respectively, which was higher than that of YOLOv5s 0.9% and 1.4%. In addition, compared with the latest target detection algorithms YOLOv7 and YOLOv8, the mAP_0.5 of improved YOLOv5s improved by 1.3% and 2.2%, respectively. Compared with the latest research results of Sim-YOLOv4 algorithm on Kitti dataset, mAP_0.5 improved 2.2%.

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更新日期/Last Update: 2024-04-29