摘要
为解决深度学习方法在高清全景图像中检测交通标志遇到图形处理器资源不足、小目标容易漏检、检测速度过慢等问题,采用小目标过采样训练数据生成方法、图像分块和几何透视检测预处理方法以及改进的轻量神经网络Improved-Tiny-YOLOv3,提出了一种基于深度学习的轻量级全景图像中交通标志检测方法。并在Tsinghua-Tencent 100K数据集上进行了实验,mAP值达到92.7%,在Nvidia 1080Ti显卡上检测速度可达到20 FPS,实验结果验证了所提方法的有效性。
In order to solve the problems of insufficient graphics processor resources,small targets being easily missed,and slow detection speed when the deep learning method is used to detect traffic signs in high-definition panoramic images,this paper proposes a lightweight panoramic image traffic sign detection method based on deep learning.It used a small target oversampling training data generation method,image segmentation and geometric perspective detection preprocessing method,and an improved lightweight neural network Improved-Tiny-YOLOv3.Experiments were performed on the Tsinghua-Tencent 100K data set,and the mAP value reached 92.7%,and the detection speed on the Nvidia 1080Ti graphics card reached 20FPS.The experimental results show the effectiveness of the proposed method.
作者
曹峻凡
张向利
闫坤
张红梅
Cao Junfan;Zhang Xiangli;Yan Kun;Zhang Hongmei(Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing,Guilin University of Electronic Technology,Guilin 541004,Guangxi,China)
出处
《计算机应用与软件》
北大核心
2024年第7期171-176,共6页
Computer Applications and Software
基金
广西无线宽带通信与信号处理重点实验室2020年主任基金项目(GXKL06200104)
广西云计算与大数据协同创新中心项目(YD1904)。
