International Journal of Circuits, Systems and Signal Processing

E-ISSN: 1998-4464
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Main Page

Submit a paper | Submission terms | Paper format


Volume 15, 2021

Title of the Paper: Depth Estimation for Monocular Image Based on Convolutional Neural Networks


Authors:  Binglin Niu, Mengxia Tang, Xuelin Chen

Pages: 533-540  

DOI: 10.46300/9106.2021.15.59     XML


Abstract: Perceiving the three-dimensional structure of the surrounding environment and analyzing it for autonomous movement is an indispensable element for robots to operate in scenes. Recovering depth information and the three-dimensional spatial structure from monocular images is a basic mission of computer vision. For the objects in the image, there are many scenes that may produce it. This paper proposes to use a supervised end-to-end network to perform depth estimation without relying on any subsequent processing operations, such as probabilistic graphic models and other extra fine steps. This paper uses an encoder-decoder structure with feature pyramid to complete the prediction of dense depth maps. The encoder adopts ResNeXt-50 network to achieve main features from the original image. The feature pyramid structure can merge high and low level information with each other, and the feature information is not lost. The decoder utilizes the transposed convolutional and the convolutional layer to connect as an up-sampling structure to expand the resolution of the output. The structure adopted in this paper is applied to the indoor dataset NYU Depth v2 to obtain better prediction results than other methods. The experimental results show that on the NYU Depth v2 dataset, our method achieves the best results on 5 indicators and the sub-optimal results on 1 indicator.