摘要
针对自动驾驶的小目标车辆漏检问题,提出了一种基于YOLOv5s改进的车辆检测算法。该算法采用加权双向特征金字塔网络(BiFPN)的融合计算方式,可以加强不同层次的信息融合,同时保留更多的浅层语义信息。通过在主干网络中引入多头自注意力机制来提升网络特征提取能力。实验结果表明:改进的网络模型与原YOLOv5s模型相比,均值平均精确率(mAP)提升了1.01%,其检测速度满足实时性需求,在不同光照条件下能够对小目标车辆进行有效的车辆目标检测。
Aiming at the problem of missing detection of small target vehicles in autonomous driving,this paper proposes an improved vehicle detection algorithm based on YOLOv5s.The algorithm adopts the weighted bidirectional feature pyramid network(BiFPN)fusion method,which can enhance the fusion of different levels of information while preserving more shallow semantic information.It also introduces multiple self-attention mechanisms into the backbone network to improve the feature extraction capability.The experimental results show that,compared with the unimproved YOLOv5s model,the mean average precision(mAP)of the improved network model increases by 1.01%.Its detection speed meets the real-time requirements,and it can effectively detect small target vehicles under different lighting conditions.
作者
张皓帝
张瑞乾
童亮
陈勇
ZHANG Haodi;ZHANG Ruiqian;TONG Liang;CHEN Yong(Mechanical Electrical Engineering School,Beijing Information Science and Technology University,Beijing 100192,China)
出处
《重庆理工大学学报（自然科学）》
北大核心
2023年第7期80-89,共10页
Journal of Chongqing University of Technology：Natural Science
基金
新能源汽车北京实验室建设项目(PXM2020_014224)。
关键词
机器视觉
小目标检测
注意力机制
特征融合
machine vision
small target detection
attention mechanisms
feature fusion
