DocumentCode :
2639473
Title :
Ultrasonic technique based on neural networks in vehicle modulation recognition
Author :
Tao, Jinsong ; Chen, Shixiu ; Yang, Li ; Hu, Yaogai
Author_Institution :
Wuhan Univ., Hubei, China
fYear :
2004
fDate :
3-6 Oct. 2004
Firstpage :
201
Lastpage :
204
Abstract :
Adapted learning rate η according to the convergence pace has been used in back-propagation neural networks (BPNN). Several vehicle recognition methods are compared and ultrasonic sensor has been given. Vehicle´s portrait section is acquired by ultrasonic sensor and the character been distilled as the BPNN´s input. Three layers BPNN with 6 inputs, 12 hidden nodes and 3 outputs run in the vehicle recognition program, and 6 different kinds of vehicles can be most correctly distinguished. The application in practice shows its recognition rate achieved 93.2%. Data is stored in server to provide different use.
Keywords :
backpropagation; neural nets; pattern recognition; road traffic; road vehicles; ultrasonic transducers; adapted learning rate; three layer backpropagation neural networks; traffic information system; ultrasonic sensor; ultrasonic technique; vehicle modulation recognition; vehicle portrait section; vehicle recognition program; Cameras; Intelligent networks; Intelligent sensors; Intelligent transportation systems; Network servers; Neural networks; Road transportation; Telecommunication traffic; Ultrasonic imaging; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on
Print_ISBN :
0-7803-8500-4
Type :
conf
DOI :
10.1109/ITSC.2004.1398897
Filename :
1398897
Link To Document :
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