摘要
本文提出了一种新的基于多因素融合的驾驶预警方法。首先,结合现有的疲劳评判因素,提出了一种基于多因素的危险评判标准,克服了传统单因素方法的应用的局限及易受外界干扰的缺点。其次,提出了一个基于SSD的检测网络,其中,用先进的MobileNetV3替换了主干网络VGG,用修改的NMS层实现了快速目标检测,最后用新设计的多任务检测器及损失函数实现了多任务检测。在预训练权重的迁移学习后,实测的检测准确率为95.7%,速度为41fps,实现了准确及实时性。
Based on multi-factor fusion,this paper proposes a new driving early warning method.First,combining the existing fatigue factors,a risk assessment criterion is proposed based on multiple factors,which overcomes traditional single-factor methods’limitations in application and shortcomings of being vulnerable to external interference.Secondly,a detection network is proposed based on SSD,in which the backbone network VGG is replaced with the advanced MobileNetV3,fast target detection is achieved with a modified NMS layer,and finally multi-task detection is realized with newly designed multi-task detectors and loss functions.After transfer-learning with the pre-training weights,the tested detection accuracy rate is 95.7%,and the speed is 41fps,which is accurate and real-time.
作者
禹江林
张云
Yu Jianglin;Zhang Yun(College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Lab for Computer Technology Applications,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2021年第11期103-108,共6页
Electronic Measurement Technology
基金
国家自然科学基金项目(61262043)
云南省科技计划项目(2011FZ029)资助。
