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- W569193786 abstract "The paper describes the development of a neural network algorithm for vehicle classification with loop detectors. The basic input for the classification is the loop analog signal which has a form typical to different vehicle classes. Seven classes of vehicles are used, namely car, van or pickup, car/van/pickup with trailer, truck, bus, truck with semitrailer and truck with full trailer. The analog loop signals are first preprocessed to form feature vectors of twenty elements. A 12*12 feature map is trained byself-organised learning with a sample of about 1400 field observations. The resulting weight factors are corrected by vector quantisation. Finally, the vehicle length is included as a limiting factor. The results of a field test indicate that neural networks can be successively used in the vehicle classification. Several possibilities for further development of the method are also available. (a) For the covering abstract of this conference see IRRD abstract no. 861222." @default.
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- W569193786 date "1994-01-01" @default.
- W569193786 modified "2023-09-27" @default.
- W569193786 title "A NEURAL NETWORK APPROACH TO VEHICLE CLASSIFICATION WITH DOUBLE INDUCTIONLOOPS" @default.
- W569193786 hasPublicationYear "1994" @default.
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