Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths
This paper introduces a depth-guided 3D object detection method that enhances the feature extraction capability of the backbone network through weak supervision. It combines large kernel convolution, global response normalization, and layer normalization techniques to significantly improve feature r...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10740295/ |
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| _version_ | 1846170462202101760 |
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| author | Xinwang Zheng Guangsong Yang Lu Yang Chengyu Lu |
| author_facet | Xinwang Zheng Guangsong Yang Lu Yang Chengyu Lu |
| author_sort | Xinwang Zheng |
| collection | DOAJ |
| description | This paper introduces a depth-guided 3D object detection method that enhances the feature extraction capability of the backbone network through weak supervision. It combines large kernel convolution, global response normalization, and layer normalization techniques to significantly improve feature robustness under weakly supervised conditions. Additionally, the depth estimation module’s feature extraction ability is bolstered by optimizing the depth-guided encoder and incorporating large-kernel depthwise separable convolutions alongside a spatial attention mechanism. On the decoder side, deformable convolutions are employed to modulate deep feature maps, reducing inference and training time while minimizing model complexity. This approach avoids the complexity associated with transformer architectures. Experiments on the KITTI 3D dataset demonstrate that the method diminishes reliance on manual labeling and can notably enhance detection accuracy while simultaneously improving processing speed. |
| format | Article |
| id | doaj-art-d1e58b1c32984e2aa573e007519fb9c6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d1e58b1c32984e2aa573e007519fb9c62024-11-12T00:01:27ZengIEEEIEEE Access2169-35362024-01-011216299016300010.1109/ACCESS.2024.348874810740295Constructing 3D Object Detectors Based on Deformable Convolutional Guided DepthsXinwang Zheng0Guangsong Yang1https://orcid.org/0000-0002-1489-9841Lu Yang2https://orcid.org/0000-0002-5395-8912Chengyu Lu3https://orcid.org/0009-0003-6180-8441Chengyi College, Jimei University, Xiamen, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen, ChinaChengyi College, Jimei University, Xiamen, ChinaChengyi College, Jimei University, Xiamen, ChinaThis paper introduces a depth-guided 3D object detection method that enhances the feature extraction capability of the backbone network through weak supervision. It combines large kernel convolution, global response normalization, and layer normalization techniques to significantly improve feature robustness under weakly supervised conditions. Additionally, the depth estimation module’s feature extraction ability is bolstered by optimizing the depth-guided encoder and incorporating large-kernel depthwise separable convolutions alongside a spatial attention mechanism. On the decoder side, deformable convolutions are employed to modulate deep feature maps, reducing inference and training time while minimizing model complexity. This approach avoids the complexity associated with transformer architectures. Experiments on the KITTI 3D dataset demonstrate that the method diminishes reliance on manual labeling and can notably enhance detection accuracy while simultaneously improving processing speed.https://ieeexplore.ieee.org/document/10740295/3D object detectiondeep learningdepth guidanceautonomous automobilesmodel refinement |
| spellingShingle | Xinwang Zheng Guangsong Yang Lu Yang Chengyu Lu Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths IEEE Access 3D object detection deep learning depth guidance autonomous automobiles model refinement |
| title | Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths |
| title_full | Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths |
| title_fullStr | Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths |
| title_full_unstemmed | Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths |
| title_short | Constructing 3D Object Detectors Based on Deformable Convolutional Guided Depths |
| title_sort | constructing 3d object detectors based on deformable convolutional guided depths |
| topic | 3D object detection deep learning depth guidance autonomous automobiles model refinement |
| url | https://ieeexplore.ieee.org/document/10740295/ |
| work_keys_str_mv | AT xinwangzheng constructing3dobjectdetectorsbasedondeformableconvolutionalguideddepths AT guangsongyang constructing3dobjectdetectorsbasedondeformableconvolutionalguideddepths AT luyang constructing3dobjectdetectorsbasedondeformableconvolutionalguideddepths AT chengyulu constructing3dobjectdetectorsbasedondeformableconvolutionalguideddepths |