| LW-Net: A Lightweight Network for Monocular Depth Estimation | |
Feng, Cheng1; Zhang, Congxuan1,2; Chen, Zhen1; Li, Ming1 ; Chen, Hao1; Fan, Bingbing1
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| 发表期刊 | IEEE ACCESS
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| ISSN | 2169-3536 |
| 2020 | |
| 卷号 | 8页码:196287-196298 |
| 通讯作者 | Zhang, Congxuan(zcxdsg@163.com) |
| 摘要 | Existing self-supervised monocular depth estimation methods usually explore increasingly large networks to achieve accurate estimation results. However, larger networks are more difficult to train and require more storage space. To balance the network size and the computational accuracy, we propose in this article a compact lightweight network for monocular depth estimation, named LW-Net. First, we construct a compact network by designing an iterative decoder with shared weights and a lightweight pyramid encoder. The proposed network includes significantly fewer parameters than most of the existing monocular depth estimation networks. Second, we exploit a self-supervised training strategy by combining the proposed LW-Net model with a pose network, and we then use a hybrid loss function to train the decoder and encoder separately. The proposed training strategy results in the LW-Net model achieving a better performance in terms of estimation accuracy than other methods. Finally, we respectively run the proposed LW-Net model on the KITTI and Make3D datasets to conduct a comprehensive comparison with several state-of-the-art methods. The experimental results demonstrate that our method performs the best in terms of computational accuracy while utilizing the fewest parameters. Specifically, the model parameters of our method are reduced by 46.6%, the time cost is decreased by 7.69%, and the frame rate is increased by 5.19% compared with the existing state-of-the-art method. |
| 关键词 | Estimation Decoding Computational modeling Cameras Task analysis Robots Training Monocular depth estimation lightweight self-supervised learning iterative decoder convolutional neural networks |
| DOI | 10.1109/ACCESS.2020.3034751 |
| 关键词[WOS] | SHAPE |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Key Research and Development Program of China[2020YFC2003800] ; National Natural Science Foundation of China[61772255] ; National Natural Science Foundation of China[61866026] ; National Natural Science Foundation of China[61866025] ; Advantage Subject Team Project of Jiangxi Province[20165BCB19007] ; Outstanding Young Talents Program of Jiangxi Province[20192BCB23011] ; National Natural Science Foundation of Jiangxi Province[20202ACB214007] ; Aeronautical Science Foundation of China[2018ZC56008] ; China Postdoctoral Science Foundation[2019M650894] ; Innovation Fund Designated for Graduate Students of Nanchang Hangkong University[YC2019038] |
| 项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Advantage Subject Team Project of Jiangxi Province ; Outstanding Young Talents Program of Jiangxi Province ; National Natural Science Foundation of Jiangxi Province ; Aeronautical Science Foundation of China ; China Postdoctoral Science Foundation ; Innovation Fund Designated for Graduate Students of Nanchang Hangkong University |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:000589763300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/41781 |
| 专题 | 管理与支撑部门_科技处 |
| 通讯作者 | Zhang, Congxuan |
| 作者单位 | 1.Nanchang Hangkong Univ, Key Lab Nondestruct Testing, Minist Educ, Nanchang 330063, Jiangxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100000, Peoples R China |
| 通讯作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Feng, Cheng,Zhang, Congxuan,Chen, Zhen,et al. LW-Net: A Lightweight Network for Monocular Depth Estimation[J]. IEEE ACCESS,2020,8:196287-196298. |
| APA | Feng, Cheng,Zhang, Congxuan,Chen, Zhen,Li, Ming,Chen, Hao,&Fan, Bingbing.(2020).LW-Net: A Lightweight Network for Monocular Depth Estimation.IEEE ACCESS,8,196287-196298. |
| MLA | Feng, Cheng,et al."LW-Net: A Lightweight Network for Monocular Depth Estimation".IEEE ACCESS 8(2020):196287-196298. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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