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A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning
Joint Authors
Li, Fangmin
Huang, Zhijian
Luan, Xidao
Cai, Zuowei
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution.
However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture.
Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming.
Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL.
The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset.
Then, an AL framework-embedded YOLO-v3 model is constructed.
In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy.
Experimental results show that the proposed method can effectively detect mud in ores.
More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy.
American Psychological Association (APA)
Huang, Zhijian& Li, Fangmin& Luan, Xidao& Cai, Zuowei. 2020. A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1194452
Modern Language Association (MLA)
Huang, Zhijian…[et al.]. A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1194452
American Medical Association (AMA)
Huang, Zhijian& Li, Fangmin& Luan, Xidao& Cai, Zuowei. A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1194452
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194452