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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1576-1580

Classification of mine images based on improved deep residual network

Cheng Deqianga
Wang Yuchena
Kou Qiqib
Fu Xinzhua
Chen Lianglianga
Zhao Kaia
a. School of Information & Control Engineering, b. School of Computer Science & Technology, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2020.05.0151

Abstract

Accurate coal gangue classification and recognition capabilities are the key issues for intelligent coal gangue sorting robots to solve. In the detection of coal gangue by the deep learning image classification method, in order to overcome the problems of large computational complexity, high complexity and information loss of the current residual network, this paper proposed an image classification method based on improved deep residual network. It proposed a new loss function(soft-center loss) to overcome the problems of the softmax classifier's poor ability to discriminate features and the possibility of overconfident models. At the same time, it used the CBDNet denoising network in the image preprocessing stage, which improved the quality of the underground images and further improves the accuracy of coal gangue classification. The experimental results show that compared with other classification network models, the accuracy rate of downhole image classification is improved by 4.12%, and the accuracy rate of public data set CIFAR-10 is increased by 1.5%.

Foundation Support

国家重点研发计划资助项目(2018YFC0808302)
国家自然科学基金资助项目(51774281)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.05.0151
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Technology of Graphic & Image
Pages: 1576-1580
Serial Number: 1001-3695(2021)05-056-1576-05

Publish History

[2021-05-05] Printed Article

Cite This Article

程德强, 王雨晨, 寇旗旗, 等. 基于改进深度残差网络的矿井图像分类 [J]. 计算机应用研究, 2021, 38 (5): 1576-1580. (Cheng Deqiang, Wang Yuchen, Kou Qiqi, et al. Classification of mine images based on improved deep residual network [J]. Application Research of Computers, 2021, 38 (5): 1576-1580. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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