A vehicle detection method based on disparity segmentation
The detection of small objects has always been one of the key challenges in vehicle detection. In this work, a standard for dividing the object more accurately than traditional methods is presented. Based on the division standard of disparity segmentation, we propose a novel multi-scale detection ne...
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| Published in | Multimedia tools and applications Vol. 82; no. 13; pp. 19643 - 19655 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The detection of small objects has always been one of the key challenges in vehicle detection. In this work, a standard for dividing the object more accurately than traditional methods is presented. Based on the division standard of disparity segmentation, we propose a novel multi-scale detection network aiming to reduce the transmission of redundant information between each scale. We divide the objects by depth, which is the distance from the object to the viewpoint. Then, a multi-branch architecture providing specialized detection for objects of each scale separately is constructed. Through ablation experiments, our method achieves an increase of 1.63 to 2.01 mAP compared with the baseline method. On the KITTI dataset, our method combined with state-of-arts achieves an increase of 3.54 mAP for small objects and 0.79 mAP for medium objects. |
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| AbstractList | The detection of small objects has always been one of the key challenges in vehicle detection. In this work, a standard for dividing the object more accurately than traditional methods is presented. Based on the division standard of disparity segmentation, we propose a novel multi-scale detection network aiming to reduce the transmission of redundant information between each scale. We divide the objects by depth, which is the distance from the object to the viewpoint. Then, a multi-branch architecture providing specialized detection for objects of each scale separately is constructed. Through ablation experiments, our method achieves an increase of 1.63 to 2.01 mAP compared with the baseline method. On the KITTI dataset, our method combined with state-of-arts achieves an increase of 3.54 mAP for small objects and 0.79 mAP for medium objects. |
| Author | Bu, Wanghui Shi, Xiaoying Li, Shiyang Chen, Jing Peng, Weimin |
| Author_xml | – sequence: 1 givenname: Shiyang surname: Li fullname: Li, Shiyang organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 2 givenname: Jing orcidid: 0000-0003-3127-8462 surname: Chen fullname: Chen, Jing email: cj@hdu.edu.cn organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 3 givenname: Weimin surname: Peng fullname: Peng, Weimin organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 4 givenname: Xiaoying surname: Shi fullname: Shi, Xiaoying organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 5 givenname: Wanghui surname: Bu fullname: Bu, Wanghui organization: School of Mechanical Engineering, Tongji Univerity |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| SubjectTerms | Ablation Accuracy Computer Communication Networks Computer Science Data Structures and Information Theory Methods Multimedia Multimedia Information Systems Proposals Segmentation Sensors Special Purpose and Application-Based Systems |
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| Title | A vehicle detection method based on disparity segmentation |
| URI | https://link.springer.com/article/10.1007/s11042-023-14360-x https://www.proquest.com/docview/2804513023 |
| Volume | 82 |
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