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LW-Net: A Lightweight Network for Monocular Depth Estimation
- Source :
- IEEE Access, Vol 8, Pp 196287-196298 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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.
- Subjects :
- iterative decoder
General Computer Science
Computer science
02 engineering and technology
010501 environmental sciences
01 natural sciences
self-supervised learning
convolutional neural networks
Pyramid
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Pyramid (image processing)
lightweight
0105 earth and related environmental sciences
Monocular
General Engineering
Function (mathematics)
Construct (python library)
Frame rate
Robot
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
lcsh:TK1-9971
Encoder
Algorithm
Decoding methods
Monocular depth estimation
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....bf031f77eb1ba8d4ff62be88c815851f
- Full Text :
- https://doi.org/10.1109/access.2020.3034751