Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network
Joint Authors
Yuan, Jianzhong
Zhou, Wujie
Lv, Sijia
Chen, Yuzhen
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
In order to obtain the distances between the surrounding objects and the vehicle in the traffic scene in front of the vehicle, a monocular visual depth estimation method based on the depthwise separable convolutional neural network is proposed in this study.
First, features containing shallow depth information were extracted from the RGB images using the convolution layers and maximum pooling layers.
Subsampling operations were also performed on these images.
Subsequently, features containing advanced depth information were extracted using a block based on an ensemble of convolution layers and a block based on depth separable convolution layers.
The output from all different blocks is combined afterwards.
Finally, transposed convolution layers were used for upsampling the feature maps to the same size with the original RGB image.
During the upsampling process, skip connections were used to merge the features containing shallow depth information that was obtained from the convolution operation through the depthwise separable convolution layers.
The depthwise separable convolution layers can provide more accurate depth information features for estimating the monocular visual depth.
At the same time, they require reduced computational cost and fewer parameter numbers while providing a similar level (or slightly better) computing performance.
Integrating multiple simple convolutions into a block not only increases the overall depth of the neural network but also enables a more accurate extraction of the advanced features in the neural network.
Combining the output from multiple blocks can prevent the loss of features containing important depth information.
The testing results show that the depthwise separable convolutional neural network provides a superior performance than the other monocular visual depth estimation methods.
Therefore, applying depthwise separable convolution layers in the neural network is a more effective and accurate approach for estimating the visual depth.
American Psychological Association (APA)
Yuan, Jianzhong& Zhou, Wujie& Lv, Sijia& Chen, Yuzhen. 2019. Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1173868
Modern Language Association (MLA)
Yuan, Jianzhong…[et al.]. Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1173868
American Medical Association (AMA)
Yuan, Jianzhong& Zhou, Wujie& Lv, Sijia& Chen, Yuzhen. Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1173868
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173868