Incorporating Plane-Sweep in Convolutional Neural Network Stereo Imaging for Road Surface Reconstruction

AbstractThis paper covers the problem of road surface reconstruction by stereo vision with cameras placed behind the windshield of a moving vehicle. An algorithm was developed that employs a plane-sweep approach and uses semi-global matching for optimization. Different similarity measures were evaluated for the task of matching pixels, namely mutual information, background subtraction by bilateral filtering, and Census. The chosen sweeping direction is the plane normal of the mean road surface. Since the cameras’ position in relation to the base plane is continuously changing due to the suspension of the vehicle, the search for the base plane was integrated into the stereo algorithm. Experiments were conducted for different types of pavement and different lighting conditions. Results are presented for the target application of road surface reconstruction, and they show high correspondence to laser scan reference measurements. The method handles motion blur well, and elevation maps are reconstructed on a millimeter-scale, while images are captured at driving speed.

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LV-GCNN: A lossless voxelization integrated graph convolutional neural network for surface reconstruction from point clouds

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102504 ◽

2021 ◽

Vol 103 ◽

pp. 102504

Author(s):

Hangbin Wu ◽

Zeran Xu ◽

Chun Liu ◽

Akram Akbar ◽

Han Yue ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Surface Reconstruction ◽

Point Clouds

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Winter Road Surface Conditions Classification using Convolutional Neural Network (CNN): Visible Light and Thermal Images Fusion

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0613 ◽

2021 ◽

Author(s):

Ce Zhang ◽

Ehsan Nateghinia ◽

Luis Miranda-Moreno ◽

Lijun Sun

Keyword(s):

Neural Network ◽

Visible Light ◽

Convolutional Neural Network ◽

Weighted Average ◽

Road Surface ◽

Thermal Images ◽

Winter Maintenance ◽

Surface Conditions ◽

Snow Plowing ◽

Winter Road

In winter, road conditions play a crucial role in traffic flow efficiency and road safety. Icy, snowy, slushy, or wet road conditions reduce tire friction and affect vehicle stability which could lead to dangerous crashes. To keep traffic operations safe, cities spend a significant budget on winter maintenance operations such as snow plowing and spreading salt/sand. This paper proposes a methodology for automated winter road surface conditions classification using Convolutional Neural Network and the combination of thermal and visible light cameras. As part of this research, 4,244 pairs of visible light and thermal images are captured from pavement surfaces and classified into snowy, icy, wet, and slushy surface conditions. Two single-stream CNN models (visible light and thermal streams), and one dual-stream CNN model are developed. The average F1-Score of dual-stream model is 0.866, 0.935, 0.985, and 0.888 on snowy, icy, wet, and slushy, respectively. The weighted average F1-Score is 0.94.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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