摘要
在智能交通系统中,车辆目标检测有广泛应用。为了提高车辆目标检测性能,采用基于FPN的YOLOv3算法进行车辆多目标检测,并且通过添加注意力机制模块进行网络优化,提出了一种基于空间注意力机制SAM的YOLOv3车辆多目标检测优化算法,并在所构造的车辆多目标数据集上对提出的算法进行了验证,证明其对车辆多目标检测的优势。实验表明,优化后的检测算法相比原检测算法模型参数量降低了55.36%,mAP值提升了1.15%,优于原检测算法。
Vehicle target detection is widely used in intelligent transportation system.In order to improve vehicle target detection performance,the FPN-based YOLOv3 algorithm is used for vehicle multi-target detection,and the attention mechanism module is added to optimize the network.An optimized YOLOv3 vehicle multi-target detection algorithm based on spatial attention mechanism(SAM)is proposed.The proposed algorithm is verified on the constructed vehicle multi-target dataset,which proves its advantage in multi-target vehicle detection.The experimental results show that compared with the original detection algorithm,the model parameters of the optimized detection algorithm are reduced by 55.36%,and the mAP value is increased by 1.15%,which is better than the original detection algorithm.
作者
罗建晨
杨蕾
LUO Jianchen;YANG Lei(School of Electronic and Information,Zhongyuan University of Technology,Zhengzhou 450007,China)
出处
《现代信息科技》
2021年第6期103-105,108,共4页
Modern Information Technology
基金
中原科技创新领军人才(214200510013)
河南省高校重点科研项目(21A510016)
留学人员科研资助和创业启动项目(HRSS2021[36])。
