Universiti Teknologi Malaysia Institutional Repository

Deep learning stereo matching algorithm using Siamese network

Samadi, Masoud and Othman, Mohd. Fauzi and Taguchi, Akira (2019) Deep learning stereo matching algorithm using Siamese network. Journal of Electrical Engineering, 18 (3). pp. 63-67. ISSN 0128-4428


Official URL: https://dx.doi.org/10.11113/elektrika.v18n3.202


Autonomous vehicle has become a very hot topic for researchers in recent years. One of the important sensors used in these vehicles is Stereo Cameras/Vision. Stereo vision systems are used to estimate the depth from the two cameras installed on robots or vehicles. This method can deliver the 3D position of all objects captured in the scene at a lower cost and higher density compared to LIDAR. Recently, neural net-works are vastly investigated and used in image processing problems and deep learning networks which has surpassed traditional computer vision methods specially in object recognition. In this paper, we propose to use a GPU with a new Siamese deep learning method to speed up the stereo matching algorithm. In this work, we use a high end Nvidia DGX workstation to train and test our algorithm and compare the results with normal GPUs and CPUs. Based on numerical evaluation, the Nvidia DGX can train a neural network with higher input image resolution approximately 8 times faster than a normal GPU and 40 times faster than a Core i7 8 Cores CPU. Since it has the ability to train on a higher resolution the network can be trained in more iteration and results in higher accuracy.

Item Type:Article
Uncontrolled Keywords:LIDAR, Stereo Vision, Siamese Deep Neural Network, GPU
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:85277
Deposited By: Fazli Masari
Deposited On:17 Mar 2020 08:10
Last Modified:17 Mar 2020 08:10

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