Lightweight Self-supervised Monocular Depth Estimation Method with Enhanced Direction-aware

CHENG Deqiang; XU Shuai; LÜ Chen; HAN Chenggong; JIANG He; KOU Qiqi

doi:10.11999/JEIT240189

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3683-3692

CHENG Deqiang, XU Shuai, LÜ Chen, HAN Chenggong, JIANG He, KOU Qiqi. Lightweight Self-supervised Monocular Depth Estimation Method with Enhanced Direction-aware[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3683-3692. doi: 10.11999/JEIT240189

Citation:

CHENG Deqiang, XU Shuai, LÜ Chen, HAN Chenggong, JIANG He, KOU Qiqi. Lightweight Self-supervised Monocular Depth Estimation Method with Enhanced Direction-aware[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3683-3692. doi: 10.11999/JEIT240189

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Lightweight Self-supervised Monocular Depth Estimation Method with Enhanced Direction-aware

doi: 10.11999/JEIT240189

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (52304182), The Promoting Science and Technology Innovation Special Funds Program of Xuzhou City (KC23401)

Received Date: 2024-03-20
Rev Recd Date: 2024-06-27

Available Online: 2024-07-05

Publish Date: 2024-09-26

Abstract

Abstract

To address challenges such as high complexity in monocular depth estimation networks and low accuracy in regions with weak textures, a Direction-Aware Enhancement-based lightweight self-supervised monocular depth estimation Network (DAEN) is proposed in this paper. Firstly, the Iterative Dilated Convolution module (IDC) is introduced as the core of the encoder to extract correlations among distant pixels. Secondly, the Directional Awareness Enhancement module (DAE) is designed to enhance feature extraction in the vertical direction, providing the depth estimation model with additional depth cues. Furthermore, the problem of detail loss during the decoder upsampling process is addressed through the aggregation of disparity map features. Lastly, the Feature Attention Module (FAM) is employed to connect the encoder and decoder, effectively leveraging global contextual information to resolve adaptability issues in regions with weak textures. Experimental results on the KITTI dataset demonstrate that the proposed method has a model parameter count of only 2.9M, achieving an advanced performance with $ \delta $ metric of 89.2%. The generalization of DAEN is validated on the Make3D datasets, with results indicating that the proposed method outperforms current state-of-the-art methods across various metrics, particularly exhibiting superior depth prediction performance in regions with weak textures.
- Image processing,
- Depth estimation,
- Self-supervised learning,
- Direction-aware

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References(22)

References

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