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基于改进YOLOv5及危险区域判断的碰撞预警系统研究

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为提升碰撞预警系统对周围环境的感知能力，提出一种基于YOLOv5及危险区域判断的碰撞预警系统。首先，通过通道注意力模块提高模型的判别能力和准确性，然后，使用路径聚合网络与空间金字塔池化提高模型对多尺度特征的提取能力，最后，通过引入预警激活区域过滤相对安全的目标，提高了预警系统的预警精确度。结果表明，引入预警激活区域后，与无预警激活区域相比，预警系统的准确度、精度和召回率分别提高20％、50％和26。7％，运行速度提升49。1％，进一步证明了方法的有效性。
Research on Collision Warning System Based on Improved YOLOv5 &Hazardous Zone Judgment
In order to improve the ability of the collision warning system to perceive the surrounding environment,this paper proposed a collision warning system based on YOLOv5 and hazardous area judgment.Firstly,the discriminative ability and accuracy of the model were improved by the channel attention module,then,the extraction ability of the model for multi-size features was improved by using path aggregation network and spatial pyramid pooling,and finally,the warning accuracy of the warning system was improved by filtering relatively safe targets through the introduction of warning activation regions.The results show that the introduction of warning activation regions improves the accuracy,precision and recall of the warning system by 20％,50％and 26.7％,respectively,the running speed is increased by 49.1％,which further proves the effectiveness of the method.

YOLOv5Channel attention modulePath aggregation networkSpatial pyramid poolingWarning activation areaCollision warning system

衣振兴、詹振飞、毛青、孙博文、王菊

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重庆交通大学,重庆 400074

汽车噪声振动和安全技术国家重点实验室,重庆 401120

YOLOv5 通道注意力模块 路径聚合网络 空间金字塔池化 预警激活区域 碰撞预警系统

智能车辆安全技术国家重点实验室开放基金重庆交通大学-长三角先进材料研究所省级研究生联合培养基地基金

IVSTSKL-202305JDLHPYJD2021008

2024

10.19620/j.cnki.1000-3703.20240026

汽车技术
中国汽车工程学会 长春汽车研究所

汽车技术

CSTPCD北大核心
影响因子：0.522
ISSN：1000-3703
年,卷(期)：2024.(4)
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