Road Object Detection Using a Disparity-Based Fusion Model
Detection methods based on 2-D images tend to extract the color, texture, shape, and other appearance features of objects. However, in complex scenes, the detection results using these methods are often influenced by shadows, occlusion, and resolution. In this paper, a disparity-proposal-based detec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8334544/ |
| _version_ | 1818876794621657088 |
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| author | Jing Chen Wenqiang Xu Weimin Peng Wanghui Bu Baixi Xing Geng Liu |
| author_facet | Jing Chen Wenqiang Xu Weimin Peng Wanghui Bu Baixi Xing Geng Liu |
| author_sort | Jing Chen |
| collection | DOAJ |
| description | Detection methods based on 2-D images tend to extract the color, texture, shape, and other appearance features of objects. However, in complex scenes, the detection results using these methods are often influenced by shadows, occlusion, and resolution. In this paper, a disparity-proposal-based detection method that rapidly extracts candidate frames of the detection objects on the basis of stereo disparity and ensures the robustness of the candidate frames under different perturbations is proposed. Furthermore, depth information is used to construct multi-scale pooling layers, allowing objects of different sizes to activate different layers at different levels. The detection model incorporates 2-D image features and 3-D geometric features and overcomes the limitations of the 2-D detection methods (absence of depth information) by using disparity features. Based on the experimental results, this method effectively achieves on-road object detection in complex scenes. |
| first_indexed | 2024-12-19T13:48:03Z |
| format | Article |
| id | doaj.art-ea6147dfd293415c858b7faf7f534f15 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:48:03Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ea6147dfd293415c858b7faf7f534f152022-12-21T20:18:49ZengIEEEIEEE Access2169-35362018-01-016196541966310.1109/ACCESS.2018.28252298334544Road Object Detection Using a Disparity-Based Fusion ModelJing Chen0https://orcid.org/0000-0003-3127-8462Wenqiang Xu1https://orcid.org/0000-0003-3378-6719Weimin Peng2https://orcid.org/0000-0002-2486-2460Wanghui Bu3Baixi Xing4Geng Liu5School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaCollege of Economics and Management, China Jiliang University, Hangzhou, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaDetection methods based on 2-D images tend to extract the color, texture, shape, and other appearance features of objects. However, in complex scenes, the detection results using these methods are often influenced by shadows, occlusion, and resolution. In this paper, a disparity-proposal-based detection method that rapidly extracts candidate frames of the detection objects on the basis of stereo disparity and ensures the robustness of the candidate frames under different perturbations is proposed. Furthermore, depth information is used to construct multi-scale pooling layers, allowing objects of different sizes to activate different layers at different levels. The detection model incorporates 2-D image features and 3-D geometric features and overcomes the limitations of the 2-D detection methods (absence of depth information) by using disparity features. Based on the experimental results, this method effectively achieves on-road object detection in complex scenes.https://ieeexplore.ieee.org/document/8334544/Object detectionstereo visiondisparityproposalmulti-scale pooling |
| spellingShingle | Jing Chen Wenqiang Xu Weimin Peng Wanghui Bu Baixi Xing Geng Liu Road Object Detection Using a Disparity-Based Fusion Model IEEE Access Object detection stereo vision disparity proposal multi-scale pooling |
| title | Road Object Detection Using a Disparity-Based Fusion Model |
| title_full | Road Object Detection Using a Disparity-Based Fusion Model |
| title_fullStr | Road Object Detection Using a Disparity-Based Fusion Model |
| title_full_unstemmed | Road Object Detection Using a Disparity-Based Fusion Model |
| title_short | Road Object Detection Using a Disparity-Based Fusion Model |
| title_sort | road object detection using a disparity based fusion model |
| topic | Object detection stereo vision disparity proposal multi-scale pooling |
| url | https://ieeexplore.ieee.org/document/8334544/ |
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