Low-Light Imaging: A Novel Industrial Endoscope With Adaptive Analog Gain for Blast Furnaces

Zunhui Yi; Lei He; Ming Lu; Chaoyang Chen; Zhaohui Jiang; Weihua Gui

doi:10.1109/JAS.2025.125690

Volume 13 Issue 4

Apr. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(4): 796-809

Z. Yi, L. He, M. Lu, C. Chen, Z. Jiang, and W. Gui, “Low-light imaging: A novel industrial endoscope with adaptive analog gain for blast furnaces,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 796–809, Apr. 2026. doi: 10.1109/JAS.2025.125690

Citation:

Z. Yi, L. He, M. Lu, C. Chen, Z. Jiang, and W. Gui, “Low-light imaging: A novel industrial endoscope with adaptive analog gain for blast furnaces,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 796–809, Apr. 2026. doi: 10.1109/JAS.2025.125690

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Low-Light Imaging: A Novel Industrial Endoscope With Adaptive Analog Gain for Blast Furnaces

doi: 10.1109/JAS.2025.125690

Funds: This work was supported in part by the National Natural Science Foundation of China (62403191, 62403192), the Hunan Provincial Natural Science Foundation of China (2024JJ6223), and the Hainan Provincial Natural Science Foundation of China (625QN363)

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Author Bio:
Zunhui Yi received the B.S. degree in measurement-control technology and instrumentation from Shandong University in 2017, and the Ph.D. degree in control science and engineering from Central South University in 2023. He is currently a Lecturer with Hunan University of Science and Technology. His research interests include optical imaging, image processing, and key parameters perception via visual intelligence

Lei He received the B.S. degree in measurement-control technology and instrumentation from Shandong University in 2017, and the Ph.D. degree in control science and engineering from Central South University in 2023. He is currently a Lecturer with Hunan University of Science and Technology. His research interests include parameter detection of industrial process, visual intelligent perception, and image processing

Ming Lu received the M.S. degree in instrument science and technology from Central South University in 2009, and the Ph.D. degree in control science and engineering from Central South University in 2015. Since then, he has been with the School of Information and Electrical Engineering, Hunan University of Science and Technology, where he is currently a Professor. His research interests include adaptive control, modeling, optimization, control of complex systems, and intelligent equipment and intelligent manufacturing

Chaoyang Chen received the Ph.D. degree in control science and engineering from Huazhong University of Science and Technology in 2014. He was a Postdoctoral Fellow with the School of Information Science and Engineering, Central South University, from 2015 to 2017. He is currently a Professor with the School of Information and Electrical Engineering, Hunan University of Science and Technology. His current research interests include networked control systems, complex networks, multi-agent systems, robust control, and their related applications

Zhaohui Jiang (Member, IEEE) received the M.E. degree in automatic control engineering and the Ph.D. degree in control science and engineering from Central South University in 2006 and 2011, respectively. He is currently a Professor with Central South University. His research interests include detection technology and automatic equipment, image processing, industrial VR, and modeling and optimal control of complex industrial processes

Weihua Gui (Member, IEEE) received the B.E. degree in electrical engineering and the M.E. degree in automatic control engineering from Central South University in 1976 and 1981, respectively. Since 1991, he has been a Full Professor with Central South University. His research interests include modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses. He is an Academician of the Chinese Academy of Engineering
Corresponding author: Lei He, e-mail: helei_xb@hnust.edu.cn
Received Date: 2025-02-13
Revised Date: 2025-05-28
Accepted Date: 2025-07-06

Abstract

Abstract

The burden surface topography of a blast furnace is the main basis for judging the furnace conditions and plays an important role in adjusting the charging system and ensuring the stable progress of the ironmaking process. Visible light imaging technology has the potential to capture real-time high-resolution images of the burden surface, providing a wealth of burden surface topography information. However, capturing high-quality burden surface videos in a sealed environment with extremely uneven light distribution remains an urgent problem to be solved. To this end, this paper develops a novel type of industrial endoscope with an adaptive analog gain to obtain information-rich burden surface images under complex lighting conditions. Firstly, a signal conversion model of the burden surface imaging process is constructed to analyze the impact of lighting on imaging. Based on the analysis, an imaging optical system with a large relative aperture and a long optical path imaging structure is developed to address the problem of weak illumination in the burden surface area. On this basis, an automatic exposure control system with an adaptive analog gain is designed to suppress the interference of dynamic strong light on imaging. Finally, experimental and application results demonstrate that the developed industrial endoscope can significantly enhance the effect of burden surface imaging and increase the amount of burden surface topography information obtained.
- Blast furnace,
- burden surface,
- exposure control,
- imaging technology,
- industrial endoscope

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References(35)

References

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Low-Light Imaging: A Novel Industrial Endoscope With Adaptive Analog Gain for Blast Furnaces

doi: 10.1109/JAS.2025.125690

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