Investigation of traffic sign image classification for self driving car
Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of t...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf |
| _version_ | 1796995619306340352 |
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| author | Farra Herliena, Md Zin Fahmi, Samsuri |
| author_facet | Farra Herliena, Md Zin Fahmi, Samsuri |
| author_sort | Farra Herliena, Md Zin |
| collection | UMP |
| description | Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of the most critical issues for autonomous vehicles is traffic sign classification. Half occlusion, colour fade by surrounding barriers, variations in shadows, reflections on signboards during the day, and movement blurring different lighting and weather situations are some of the most typical issues that might occur when identifying and detecting traffic signs. In the classification and identification of road signs, the performance of a Convolutional Neural Network (CNN) has outperformed the same of humans. The purpose of this study is to boost the accuracy of this classification in order to minimize accidents and enhance the credibility of selfdriving vehicles. Otherwise, the ecology of traffic may be jeopardised. Using image processing and machine vision processing technologies, as well as the use of in-depth learning in target classification, the traffic sign recognition method based on CNN is studied. A traffic sign detection and classification method with high efficiency and high efficiency are proposed. The German Traffic Sign Recognition Benchmark (GTSRB) is employed to test the approach method, and the results reveal that it outperforms state-of-the-art approaches. |
| first_indexed | 2024-03-06T13:05:01Z |
| format | Conference or Workshop Item |
| id | UMPir37113 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:01Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | UMPir371132023-03-14T06:59:44Z http://umpir.ump.edu.my/id/eprint/37113/ Investigation of traffic sign image classification for self driving car Farra Herliena, Md Zin Fahmi, Samsuri T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of the most critical issues for autonomous vehicles is traffic sign classification. Half occlusion, colour fade by surrounding barriers, variations in shadows, reflections on signboards during the day, and movement blurring different lighting and weather situations are some of the most typical issues that might occur when identifying and detecting traffic signs. In the classification and identification of road signs, the performance of a Convolutional Neural Network (CNN) has outperformed the same of humans. The purpose of this study is to boost the accuracy of this classification in order to minimize accidents and enhance the credibility of selfdriving vehicles. Otherwise, the ecology of traffic may be jeopardised. Using image processing and machine vision processing technologies, as well as the use of in-depth learning in target classification, the traffic sign recognition method based on CNN is studied. A traffic sign detection and classification method with high efficiency and high efficiency are proposed. The German Traffic Sign Recognition Benchmark (GTSRB) is employed to test the approach method, and the results reveal that it outperforms state-of-the-art approaches. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf Farra Herliena, Md Zin and Fahmi, Samsuri (2022) Investigation of traffic sign image classification for self driving car. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 158.. https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Farra Herliena, Md Zin Fahmi, Samsuri Investigation of traffic sign image classification for self driving car |
| title | Investigation of traffic sign image classification for self driving car |
| title_full | Investigation of traffic sign image classification for self driving car |
| title_fullStr | Investigation of traffic sign image classification for self driving car |
| title_full_unstemmed | Investigation of traffic sign image classification for self driving car |
| title_short | Investigation of traffic sign image classification for self driving car |
| title_sort | investigation of traffic sign image classification for self driving car |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf |
| work_keys_str_mv | AT farraherlienamdzin investigationoftrafficsignimageclassificationforselfdrivingcar AT fahmisamsuri investigationoftrafficsignimageclassificationforselfdrivingcar |