Faster R-CNN: an Approach to Real-Time Object Detection

被引：0
|
作者：
Gavrilescu, Raducu [1 ]
Fosalau, Cristian [1 ]
Zet, Cristian [1 ]
Skoczylas, Marcin [2 ]
Cotovanu, David [1 ]
机构：
[1] Tech Univ Iasi, Fac Elect Engn, Iasi, Romania
[2] Bialystok Tech Univ, Fac Comp Sci, Bialystok, Poland
来源：
2018 INTERNATIONAL CONFERENCE AND EXPOSITION ON ELECTRICAL AND POWER ENGINEERING (EPE) | 2018年
关键词：
region based convolutional network; Graphics Processing Unit; Convolutional Neural Network; traffic signs detection; traffic safety;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The objective of the paper is to present an example on how to use the latest image processing algorithms to detect traffic indicators safely enough to be used while driving a car. The conclusion of the paper is that the Faster Regional based Convolutional Neural Network (Faster R-CNN) algorithm has qualities in terms of accuracy and speed that make it suitable to be used in such applications. Faster R-CNN is a result of merging Region Proposal Network (RPN) and Fast-RCNN algorithms into a single network. For increasing the video processing power, a Graphics Processing Unit (GPU) was employed for training and testing at a speed of 15 fps on a dataset containing 3000 images for 4 classes. The dataset is composed of images containing the three phases of a traffic light and the STOP indicator.
引用
收藏
页码：165 / 168
页数：4
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