RGB-D Indoor Object Recognition Algorithm Based on Fusion Convolutional Neural Network

Aiming at the problems of insufficient network fusion and low detection efficiency in current object recognition using RGB-D images, a recognition algorithm based on the medium-level layer-by-layer fusion of dual-channel networks is proposed. First of all, the RGB and Depth networks are trained with...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1267; no. 1; pp. 12022 - 12028
Main Authors: Wang, Decheng, Yi, Hui, Zhao, Feng
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.07.2019
Subjects:
Accuracy
Algorithms
Artificial neural networks
Object recognition
Physics
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Aiming at the problems of insufficient network fusion and low detection efficiency in current object recognition using RGB-D images, a recognition algorithm based on the medium-level layer-by-layer fusion of dual-channel networks is proposed. First of all, the RGB and Depth networks are trained with ten labelled RGB-D indoor objects respectively, and then determine the fusion coefficients according to the identify accuracy of two types networks. Finally, two kinds of features are merged in convolutional layers step by step to obtain the final weights. By testing on the challenging NYU Depth v2 dataset, we found that the recognition accuracy of our method is 92.85%, and average detection time is 61.03ms per image. Through comparison experiments, we got the conclusion that average accuracy of the RGB-D layer-by-layer fusion network is 5.22% higher than that of the RGB network.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1267/1/012022