Channel Interaction and Transformer Depth Estimation Network: Robust Self-Supervised Depth Estimation Under Varied Weather Conditions

Jianqiang Liu, Zhengyu Guo, Peng Ping (), Hao Zhang and Quan Shi
Additional contact information
Jianqiang Liu: School of Information Science and Technology, Nantong University, Nantong 226001, China
Zhengyu Guo: School of Future Technology, South China University of Technology, Guangzhou 510641, China
Peng Ping: School of Transportation and Civil Engineering, Nantong University, Nantong 226001, China
Hao Zhang: Henan Airport Group, Zhengzhou 450002, China
Quan Shi: School of Transportation and Civil Engineering, Nantong University, Nantong 226001, China

Sustainability, 2024, vol. 16, issue 20, 1-20

Abstract: Monocular depth estimation provides low-cost environmental information for intelligent systems such as autonomous vehicles and robots, supporting sustainable development by reducing reliance on expensive, energy-intensive sensors and making technology more accessible and efficient. However, in practical applications, monocular vision is highly susceptible to adverse weather conditions, significantly reducing depth perception accuracy and limiting its ability to deliver reliable environmental information. To improve the robustness of monocular depth estimation in challenging weather, this paper first utilizes generative models to adjust image exposure and generate synthetic images of rainy, foggy, and nighttime scenes, enriching the diversity of the training data. Next, a channel interaction module and Multi-Scale Fusion Module are introduced. The former enhances information exchange between channels, while the latter effectively integrates multi-level feature information. Finally, an enhanced consistency loss is added to the loss function to prevent the depth estimation bias caused by data augmentation. Experiments on datasets such as DrivingStereo, Foggy CityScapes, and NuScenes-Night demonstrate that our method, CIT-Depth, exhibits superior generalization across various complex conditions.

Keywords: self-supervised depth estimation; feature enhancement; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/20/9131/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/20/9131/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:20:p:9131-:d:1503449

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().