Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)

Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi (2018) Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS). In: Journal of Physics: Conference Series: 2nd International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2018) , 26 - 29 April 2018 , Osaka, Japan. pp. 1-6., 1061 (1). ISSN 1742-6588

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Abstract

Automated Toll Collection System (ATCS) is one of the technologies to fulfill the Intelligent Transportation System’s (ITS) aim in providing an efficient road and transportation infrastructure at the expressway. This paper is aimed to provide an accurate and efficient ATCS based on a vehicle type classification method rather than the current implementation of toll collection that rely on sensor-based and human observation. To fulfill the aim, we proposed to implement SF-CNNLS framework to extract vehicle’s features and classify it into class 1 (passenger vehicle), class 2 (lorry) and class 4 (taxi). This ATCS is aimed to enhance the efficiency of the toll collection in Malaysia. The biggest challenge in this research is how to discriminate features of class 4 as a different class of class 1 since both classes have almost identical features. However, with our proposed method, we able to classify the vehicle with the average accuracy of 90.83 %. We tested our method using a frontal view of a vehicle from the self-obtained database (SPINT) taken using mounted-camera at the toll booth and compare the classification performance with a benchmark database named BIT.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Toll collection system; Vehicle type classification; Skipping strategy
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 21 Sep 2018 02:16
Last Modified: 08 Jan 2024 01:44
URI: http://umpir.ump.edu.my/id/eprint/21980
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