Edge-Enhanced Dual-Stream Perception Network for Monocular Depth Estimation

Estimating depth from a single RGB image has a wide range of applications, such as in robot navigation and autonomous driving. Currently, Convolutional Neural Networks based on encoder–decoder architecture are the most popular methods to estimate depth maps. However, convolutional operators have lim...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Electronics (Basel) Ročník 13; číslo 9; s. 1652
Hlavní autoři: Liu, Zihang, Wang, Quande
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2024
Témata:
Accuracy
Algorithms
Analysis
Artificial neural networks
Autonomous navigation
Coders
Datasets
Deep learning
Estimation
Neural networks
Robots
Germany
ISSN:2079-9292, 2079-9292
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Estimating depth from a single RGB image has a wide range of applications, such as in robot navigation and autonomous driving. Currently, Convolutional Neural Networks based on encoder–decoder architecture are the most popular methods to estimate depth maps. However, convolutional operators have limitations in modeling large-scale dependence, often leading to inaccurate depth predictions at object edges. To address these issues, a new edge-enhanced dual-stream monocular depth estimation method is introduced in this paper. ResNet and Swin Transformer are combined to better extract global and local features, which benefits the estimation of the depth map. To better integrate the information from the two branches of the encoder and the shallow branch of the decoder, we designed a lightweight decoder based on the multi-head Cross-Attention Module. Furthermore, in order to improve the boundary clarity of objects in the depth map, a loss function with an additional penalty for depth estimation error on the edges of objects is presented. The results on three datasets, NYU Depth V2, KITTI, and SUN RGB-D, show that the method presented in this paper achieves better performance for monocular depth estimation. Additionally, it has good generalization capabilities for various scenarios and real-world images.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13091652