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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1910-1915

Dark light image enhancement network for machine vision perception

Feng Xin1
Wang Siping1
Zhang Zhixian1
Jiao Xiaoning1
Xue Minglong1,2
1. School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
2. State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210046, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0404

Abstract

Target detection in adverse conditions such as low illumination has always been a challenging. The factors of low light and fog can lead to reduced visibility and increased noise in images, significantly disrupting the precision of object detection. To address these issues, this paper proposed and integrated a low-light image enhancement network for machine vision perception, MVP-Net, with the YOLOv3 object detection network to construct an end-to-end enhancement detection framework, MVPYOLO. MVP-Net employed inverse mapping network technology to transform conventional RGB images into pseudo-RAW image feature space and introduced a pseudo-ISP enhancement network, DOISP, for image enhancement. The objective of MVP-Net is to harness the potential advantages of RAW images in object detection while overcoming the limitations encountered in their direct application. The model has outperformed previous works on multiple real-world low-light datasets and is adaptable to detectors with various architectures. Its end-to-end detection framework achieves a mAP(50%) metric of 78.3%, an improvement of 1.85% over the YOLO detectors.

Foundation Support

重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0493)
重庆市技术创新与应用发展重点项目(cstc2021jscx-dxwtBX0018)
重庆市研究生科研创新项目(CYS23678)
重庆理工大学研究生教育高质量发展资助项目(gzlcx20222062,gzlcx20233218)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0404
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Technology of Graphic & Image
Pages: 1910-1915
Serial Number: 1001-3695(2024)06-045-1910-06

Publish History

[2023-11-15] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

冯欣, 王思平, 张智先, 等. 一种面向机器视觉感知的暗光图像增强网络 [J]. 计算机应用研究, 2024, 41 (6): 1910-1915. (Feng Xin, Wang Siping, Zhang Zhixian, et al. Dark light image enhancement network for machine vision perception [J]. Application Research of Computers, 2024, 41 (6): 1910-1915. )

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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