Research on the Optimal Machine Learning Classifier for Traffic Signs
Now autonomous driving is a hot topic, and the identification of traffic signs is also extremely important for autonomous driving. This paper mainly compares the difference of the Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Logistic Regression (LR) Classifier in the traffic sign c...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03014.pdf |
| _version_ | 1811337757940776960 |
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| author | Wang Boyu |
| author_facet | Wang Boyu |
| author_sort | Wang Boyu |
| collection | DOAJ |
| description | Now autonomous driving is a hot topic, and the identification of traffic signs is also extremely important for autonomous driving. This paper mainly compares the difference of the Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Logistic Regression (LR) Classifier in the traffic sign classification. The effect of the initial image processing on classification accuracy is also studied. The paper found that sharpening the image significantly improved the accuracy of the image classification. Based on the results of various situations, the author found that, in this paper, SVM is the classifier with the best classification effect, but the effect of LR classifier is not much worse than that of SVM when the image is sharpened. |
| first_indexed | 2024-04-13T17:59:37Z |
| format | Article |
| id | doaj.art-24fc60add03248d68e08997709080f2d |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-04-13T17:59:37Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-24fc60add03248d68e08997709080f2d2022-12-22T02:36:18ZengEDP SciencesSHS Web of Conferences2261-24242022-01-011440301410.1051/shsconf/202214403014shsconf_stehf2022_03014Research on the Optimal Machine Learning Classifier for Traffic SignsWang Boyu0University of Macau, Faculty of Science and TechnologyNow autonomous driving is a hot topic, and the identification of traffic signs is also extremely important for autonomous driving. This paper mainly compares the difference of the Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Logistic Regression (LR) Classifier in the traffic sign classification. The effect of the initial image processing on classification accuracy is also studied. The paper found that sharpening the image significantly improved the accuracy of the image classification. Based on the results of various situations, the author found that, in this paper, SVM is the classifier with the best classification effect, but the effect of LR classifier is not much worse than that of SVM when the image is sharpened.https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03014.pdf |
| spellingShingle | Wang Boyu Research on the Optimal Machine Learning Classifier for Traffic Signs SHS Web of Conferences |
| title | Research on the Optimal Machine Learning Classifier for Traffic Signs |
| title_full | Research on the Optimal Machine Learning Classifier for Traffic Signs |
| title_fullStr | Research on the Optimal Machine Learning Classifier for Traffic Signs |
| title_full_unstemmed | Research on the Optimal Machine Learning Classifier for Traffic Signs |
| title_short | Research on the Optimal Machine Learning Classifier for Traffic Signs |
| title_sort | research on the optimal machine learning classifier for traffic signs |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03014.pdf |
| work_keys_str_mv | AT wangboyu researchontheoptimalmachinelearningclassifierfortrafficsigns |