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Vehicle Detection and Type Classification Based on CNN-SVM
https://tokushima-u.repo.nii.ac.jp/records/2008123
https://tokushima-u.repo.nii.ac.jp/records/200812386ac68c6-3e1a-47fc-a786-eb10de6ffe9f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
ijmlc_11_4_304.pdf (1.21 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||
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| 公開日 | 2021-02-17 | |||||||||||||||||||
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| アクセス権 | open access | |||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||
| 関連識別子 | https://doi.org/10.18178/ijmlc.2021.11.4.1052 | |||||||||||||||||||
| 関連名称 | 10.18178/ijmlc.2021.11.4.1052 | |||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||
| タイトル | ||||||||||||||||||||
| タイトル | Vehicle Detection and Type Classification Based on CNN-SVM | |||||||||||||||||||
| 著者 |
カルンガル, スティフィン ギディンシ
× カルンガル, スティフィン ギディンシ
WEKO
1240
× Dongyang, Lyu
× 寺田, 賢治
WEKO
106
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| 抄録 | ||||||||||||||||||||
| 内容記述 | In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves its performance on the specific dataset. The low classification accuracy problem after template-based feature extraction is solved using the optimal feature description extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has a better performance in vehicle detection and type classification. | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | Vehicle detection | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | vehicle classification | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | Yolov2-tiny | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | AlexNet | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | spatial pyramid pooling | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | CNN | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | SVM | |||||||||||||||||||
| 書誌情報 |
en : International Journal of Machine Learning and Computing 巻 11, 号 4, p. 304-310, 発行日 2021-07 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||
| 収録物識別子 | 20103700 | |||||||||||||||||||
| 権利情報 | ||||||||||||||||||||
| 権利情報 | © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0). | |||||||||||||||||||
| EID | ||||||||||||||||||||
| 識別子 | 371056 | |||||||||||||||||||
| 言語 | ||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||
