Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy
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-CNNL...
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Online Access: | http://umpir.ump.edu.my/id/eprint/30744/8/Vehicle%20Type%20Classification%20Using%20an%20Enhanced%20Sparse-Filtered%20Convolutional%20Neural%20Network%20with%20Layer-Skipping%20Strategy.pdf http://umpir.ump.edu.my/id/eprint/30744/ https://doi.org/10.1109/ACCESS.2019.2963486 https://doi.org/10.1109/ACCESS.2019.2963486 |
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my.ump.umpir.307442021-02-18T09:09:19Z http://umpir.ump.edu.my/id/eprint/30744/ Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Rahman, Md. Arafatur T Technology (General) 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. IEEE 2020-01-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30744/8/Vehicle%20Type%20Classification%20Using%20an%20Enhanced%20Sparse-Filtered%20Convolutional%20Neural%20Network%20with%20Layer-Skipping%20Strategy.pdf 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 https://doi.org/10.1109/ACCESS.2019.2963486 https://doi.org/10.1109/ACCESS.2019.2963486 |
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T Technology (General) Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Rahman, Md. Arafatur Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
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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. |
format |
Article |
author |
Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Rahman, Md. Arafatur |
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Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Rahman, Md. Arafatur |
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Suryanti, Awang |
title |
Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
title_short |
Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
title_full |
Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
title_fullStr |
Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
title_full_unstemmed |
Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
title_sort |
vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy |
publisher |
IEEE |
publishDate |
2020 |
url |
http://umpir.ump.edu.my/id/eprint/30744/8/Vehicle%20Type%20Classification%20Using%20an%20Enhanced%20Sparse-Filtered%20Convolutional%20Neural%20Network%20with%20Layer-Skipping%20Strategy.pdf http://umpir.ump.edu.my/id/eprint/30744/ https://doi.org/10.1109/ACCESS.2019.2963486 https://doi.org/10.1109/ACCESS.2019.2963486 |
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1692991972757733376 |
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13.18916 |