摘要
提出了一种应用于嵌入式图形处理器(GPU)的实时目标检测算法。针对嵌入式平台计算单元较少、处理速度较慢的现状,提出了一种基于YOLO-V3(You Only Look Once-Version 3)架构的改进的轻量目标检测模型,对汽车目标进行了离线训练,在嵌入式平台上部署训练好的模型,实现了在线检测。实验结果表明,在嵌入式平台上,所提方法对分辨率为640 pixel×480 pixel的视频图像的检测速度大于23 frame/s。
A real-time target detection algorithm is proposed and used in the embedded graphic processing unit(GPU).In view of the lack of computing units and the slow processing speed for an embedded platform,an improved lightweight target detection model is proposed based on the YOLO-V3(You Only Look Once-Version 3)structure.This model is first trained off-line with vehicle targets and then deployed on the embedded GPU platform to achieve the online prediction.The experimental results show that the processing speed of the proposed method on the embedded GPU platform reaches 23 frame/s for a 640 pixel×480 pixel video.
作者
王晓青
王向军
Wang Xiaoqing;Wang Xiangjun(State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin University,Tianjin 300072,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2019年第3期274-280,共7页
Acta Optica Sinica
基金
国家自然科学基金面上项目(51575388)
关键词
机器视觉
目标检测
卷积神经网络
嵌入式平台
图形处理器
machine vision
target detection
convolutional neural network
embedded platform
graphic processing unit
