Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks
Agricultural robots; Autonomous vehicles; Convolution; Convolutional neural networks; Deep learning; Deep neural networks; Image recognition; Learning systems; Robotics; Transfer learning; Computer vision techniques; Detection methods; Different class; Feature-based; Learning methods; Learning techn...
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Institute of Electrical and Electronics Engineers Inc.
2023
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| _version_ | 1833351411270680576 |
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| author | How D.N.T. Sahari K.S.M. Hou Y.C. Basubeit O.G.S. |
| author2 | 57212923888 |
| author_facet | 57212923888 How D.N.T. Sahari K.S.M. Hou Y.C. Basubeit O.G.S. |
| author_sort | How D.N.T. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Agricultural robots; Autonomous vehicles; Convolution; Convolutional neural networks; Deep learning; Deep neural networks; Image recognition; Learning systems; Robotics; Transfer learning; Computer vision techniques; Detection methods; Different class; Feature-based; Learning methods; Learning techniques; Traffic junctions; Traffic sign recognition; Traffic signs |
| format | Conference Paper |
| id | my.uniten.dspace-24455 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-244552023-05-29T15:23:39Z Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks How D.N.T. Sahari K.S.M. Hou Y.C. Basubeit O.G.S. 57212923888 57218170038 37067465000 57216623220 Agricultural robots; Autonomous vehicles; Convolution; Convolutional neural networks; Deep learning; Deep neural networks; Image recognition; Learning systems; Robotics; Transfer learning; Computer vision techniques; Detection methods; Different class; Feature-based; Learning methods; Learning techniques; Traffic junctions; Traffic sign recognition; Traffic signs An essential component in the race towards the self-driving car is automatic traffic sign recognition. The capability to automatically recognize road signs allow self-driving cars to make prompt decisions such as adhering to speed limits, stopping at traffic junctions and so forth. Traditionally, feature-based computer vision techniques were employed to recognize traffic signs. However, recent advancements in deep learning techniques have shown to outperform traditional color and shape based detection methods. Deep convolutional neural network (DCNN) is a class of deep learning method that is most commonly applied to vision-related tasks such as traffic sign recognition. For DCNN to work well, it is imperative that the algorithm is given a vast amount of training data. However, due to the scarcity of a curated dataset of the Malaysian traffic signs, training DCNN to perform well can be very challenging. In this demonstrate that DCNN can be trained with little training data with excellent accuracy by using transfer learning. We retrain various pre-trained DCNN from other image recognition tasks by fine-tuning only the top layers on our dataset. Experiment results confirm that by using as little as 100 image samples for 5 different classes, we are able to classify hitherto traffic signs with above 90% accuracy for most pre-trained models and 98.33% for the DenseNet169 pre-trained model. � 2019 IEEE. Final 2023-05-29T07:23:39Z 2023-05-29T07:23:39Z 2019 Conference Paper 10.1109/CRC.2019.00030 2-s2.0-85084071834 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084071834&doi=10.1109%2fCRC.2019.00030&partnerID=40&md5=3da8be6f2d2afd16c62e7bc204c79fa8 https://irepository.uniten.edu.my/handle/123456789/24455 9058837 109 113 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | How D.N.T. Sahari K.S.M. Hou Y.C. Basubeit O.G.S. Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title | Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title_full | Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title_fullStr | Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title_full_unstemmed | Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title_short | Recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| title_sort | recognizing malaysia traffic signs with pre-trained deep convolutional neural networks |
| url_provider | http://dspace.uniten.edu.my/ |