A Novel Attentive Generative Adversarial Network for Waterdrop Detection and Removal of Rubber Conveyor Belt Image
Joint Authors
Li, Xianguo
Liu, Zongpeng
Li, Bin
Feng, Xinxin
Liu, Xiao
Zhou, Debao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The lens for monitoring the rubber conveyor belt is easy to adhere to a large number of water droplets, which seriously affects the image quality and then affects the effect of fault monitoring.
In this paper, a new method for detecting and removing water droplets on rubber conveyor belts based on the attentive generative adversarial network is proposed to solve this problem.
First, the water droplet image of the rubber conveyor belt is input into the generative network composed of a cyclic visual attentive network and an autoencoder with skip connections, and an image of removing water droplets and an attention map for detecting the position of the water droplet are generated.
Then, the generated image of removing water droplets is evaluated by the attentive discriminant network to assess the local consistency of the water droplet recovery area.
In order to better learn the water droplet regions and the surrounding structures during the training, the image morphology is added to the precise water droplet regions.
A dewatered rubber conveyor belt image is generated by increasing the number of circular visual attention network layers and the number of skip connection layers of the autoencoder.
Finally, a large number of comparative experiments prove the effectiveness of the water droplet image removal algorithm proposed in this paper, which outperforms of Convolutional Neural Network (CNN), Discriminative Sparse Coding (DSC), Layer Prior (LP), and Attention Generative Adversarial Network (ATTGAN).
American Psychological Association (APA)
Li, Xianguo& Liu, Zongpeng& Li, Bin& Feng, Xinxin& Liu, Xiao& Zhou, Debao. 2020. A Novel Attentive Generative Adversarial Network for Waterdrop Detection and Removal of Rubber Conveyor Belt Image. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193001
Modern Language Association (MLA)
Li, Xianguo…[et al.]. A Novel Attentive Generative Adversarial Network for Waterdrop Detection and Removal of Rubber Conveyor Belt Image. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1193001
American Medical Association (AMA)
Li, Xianguo& Liu, Zongpeng& Li, Bin& Feng, Xinxin& Liu, Xiao& Zhou, Debao. A Novel Attentive Generative Adversarial Network for Waterdrop Detection and Removal of Rubber Conveyor Belt Image. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193001
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193001