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Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy

Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi and Rahman, Md. Arafatur (2020) Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy. IEEE Access, 8. pp. 14265-14277. ISSN 2169-3536

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Abstract

In this paper, a vehicle type classification approach is proposed by using an enhanced feature extraction technique based on Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (SF-CNNLS). To extract rich and discriminant vehicle features, we introduce Three-Channels of SF-CNNLS (TC-SF-CNNLS) as the feature extraction technique. Local and global features of vehicles are extracted from three channels of an image which are, luminance and chromatic components. This technique is inspired by how human eyes differentiating objects that share almost similar features. TC-SF-CNNLS is tested with a benchmark dataset that provides frontal-view images to classify vehicle types of the bus, passenger car, taxi, minivan, SUV, and truck with Softmax Regression as a classifier. This test aims to observe the ability of this technique in differentiating vehicles with almost similar features but different classes. A test is also conducted with the self-obtained dataset (SPINT) to observe the effectiveness of this technique. The results are observed based on accuracy, precision, recall, and f-score, whereby, TCSF-NNLS has successfully recognized all the classes with an average accuracy of 0.905, precision is between 0.8629 to 0.9548, recall is between 0.83 to 0.96 and f-score is between 0.8564 to 0.9523. In addition, this technique is able to outperform other existing techniques with an average accuracy of 93.% compared to only 89.2% when 5 classes of vehicles are tested.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Vehicle type recognition; Convolutions neural network; Deep learning; Computational intelligence
Subjects: T Technology > T Technology (General)
Faculty/Division: Centre of Excellence: IBM Centre of Excellence
Centre of Excellence: Earth Resources & Sustainability Centre (ERAS)
Faculty of Computing
Depositing User: Dr. Suryanti Awang
Date Deposited: 18 Feb 2021 09:09
Last Modified: 18 Feb 2021 09:09
URI: http://umpir.ump.edu.my/id/eprint/30744
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