Skip to main content
SpringerLink
Log in

Adaptive uneven illumination correction method for autonomous live-line maintenance robot

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the development of the robot in electricity, more and more autonomous live-line maintenance robots (ALMRs) have been developed and put into use. However, in outdoor environment, complicated and uneven illumination lead to huge challenges for visual feedback and target recognition of ALMRs. Aiming at easing the disturbance brought by the strong uneven illumination, we collect a Hot-Line dataset containing fieldwork photos of the ALMR and propose an image enhancement method for uneven illumination images based on image brightness segmentation and multi-methods fusion. Through image segmentation based on illuminance, the proposed algorithm enhances the over- and under-illuminated parts of the image differently while taking the approximate illumination component as a reference. We introduce an adaptive weighted summation strategy to ease the problem of edge transition in the output. The proposed algorithm improves the overall performance of a fieldwork image of ALMR properly, making the image clearer and better. For six indexes (Laplacian, SMD2, Energy of Gradient (EOG), and Entropy for image clarity; Structural similarity index measure (SSIM) and Peak signal-to-noise ratio (PSNR) for the degree of image information retention), the proposed method provided good results on both our Hot-Line dataset (for example, on EOG, the proposed method achieves nearly double the performance index value than CLAHE) and other image datasets, and finished the enhancement within a relatively short time (within 0.02s with image size 275 × 275). The proposed algorithm has been verified on an ALMR for the power distribution network and archived good results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://sites.google.com/site/vonikakis/datasets

References

  1. Allan J-F (2012) Robotics for distribution power lines: overview of the last decade. In 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI): IEEE, pp 96–101

  2. Aracil R et al (1995) ROBTET: A new teleoperated system for live-line maintenance. In Proceedings of ESMO'95-1995 IEEE 7th International Conference on Transmission and Distribution Construction, Operation and Live-Line Maintenance IEEE pp 205–211

  3. Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Inf Syst 13(3):329–351. https://doi.org/10.1080/17517575.2018.1557256

    Article  Google Scholar 

  4. Bhatti UA et al (2020) Hybrid watermarking algorithm using clifford algebra with arnold scrambling and chaotic encryption. IEEE Access 8:76386–76398. https://doi.org/10.1109/ACCESS.2020.2988298

    Article  Google Scholar 

  5. Bhatti UA et al (2021) Advanced color edge detection using clifford algebra in satellite images. IEEE Photon J 13(2):1–20. https://doi.org/10.1109/JPHOT.2021.3059703

    Article  Google Scholar 

  6. Bhatti UA et al (2022) Local similarity-based spatia-spectral fusion hyperspectral image classification with deep CNN and gabor filtering. IEEE Trans Geosci Remote Sens 60:1–15. https://doi.org/10.1109/TGRS.2021.3090410

    Article  Google Scholar 

  7. Celik T, Tjahjadi T (2011) Contextual and variational contrast enhancement. IEEE Trans Image Process 20(12):3431–3441

    Article  MathSciNet  MATH  Google Scholar 

  8. Chang Y, Jung C, Ke P, Song H, Hwang J (2018) Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6:11782–11792. https://doi.org/10.1109/ACCESS.2018.2797872

    Article  Google Scholar 

  9. Chen Y, Zhu J, Xu M, Zhang H, Tang X, Dong E (2019) Application of haptic virtual fixtures on hot-line work robot-assisted manipulation. Presented at the intelligent robotics and applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part IV, Shenyang, China. [Online]. Available: https://doi.org/10.1007/978-3-030-27538-9_19

  10. Dale-Jones R, Tjahjadi T (1993) A study and modification of the local histogram equalization algorithm. Pattern Recogn 26(9):1373–1381

    Article  Google Scholar 

  11. Ebner M (2007) Colour constancy. John Wiley & Sons Ltd, Chichester

    Google Scholar 

  12. Gharbi M, Chen JW, Barron JT, Hasinoff SW, Durand F (2017) Deep bilateral learning for real-time image enhancement. (In English), Acm Transactions on Graphics, vol 36, no 4, Jul Artn 11810.1145/3072959.3073592

  13. Gonzalez RC, Woods RE (2002) Digital image processing. ed: Prentice hall Upper Saddle River, NJ

  14. Goswami S, Singh SK (2020) A simple deep learning based image illumination correction method for paintings. Pattern Recogn Lett 138:392–396

    Article  Google Scholar 

  15. Guo T, Wei Y, Shao H, Ma B (2021) Research on underwater target detection method based on improved MSRCP and YOLOv3. In 2021 IEEE International Conference on Mechatronics and Automation (ICMA), 8-11 Aug. 2021, pp 1158–1163. https://doi.org/10.1109/ICMA52036.2021.9512827

  16. Hu Y, Wang B, Lin S (2017) Fc4: fully convolutional color constancy with confidence-weighted pooling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4085–4094

  17. Huynh-The T, Le B-V, Lee S, Le-Tien T, Yoon Y (2014) Using weighted dynamic range for histogram equalization to improve the image contrast. EURASIP J Image Video Process 2014(1):44. https://doi.org/10.1186/1687-5281-2014-44

    Article  Google Scholar 

  18. Jian L, Jianjun S, Mengchao F, Shouyin L, Xu D (2016) Research and application of the water washing robot with hot-line working used in 220kV open type substation. In 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI), 11–13 Oct 2016, pp 1–5. https://doi.org/10.1109/CARPI.2016.7745613

  19. Jobson DJ, Rahman Z-U, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451–462

    Article  Google Scholar 

  20. Jobson DJ, Rahman Z-U, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–976

    Article  Google Scholar 

  21. Kim Y-T (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consumer Electron 43(1):1–8

    Article  Google Scholar 

  22. Land EH, McCann JJ (1971) Lightness and retinex theory. Josa 61(1):1–11

    Article  Google Scholar 

  23. Li Y-F, Chen N-N, Zhang J-C (2010) Fast and high sensitivity focusing evaluation function. Appl Res Comput 4:1534–1536

    Google Scholar 

  24. Li C, Liu J, Wu Q, Bi L (2021) An adaptive enhancement method for low illumination color images. Appl Intell 51(1):202–222. https://doi.org/10.1007/s10489-020-01792-3

    Article  Google Scholar 

  25. Liu Z-C, Wang D-W, Liu Y, Liu X-J (2016) Adaptive adjustment algorithm for non-uniform illumination images based on 2D gamma function. Trans Beijing Inst Technol 36(2):191–196

    Google Scholar 

  26. Lv X, Lu S (2012) Design and implementation of Hot-line Working Robot master-slave manipulator. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 11–14 Dec. 2012, pp 2282–2287. https://doi.org/10.1109/ROBIO.2012.6491309

  27. Mu Q, Wang X, Wei Y, Li Z (2021) Low and non-uniform illumination color image enhancement using weighted guided image filtering. Comput Vis Media 7(4):529–546. https://doi.org/10.1007/s41095-021-0232-x

    Article  Google Scholar 

  28. Nakashima M, Yakabe H, Maruyama Y, Yano K, Morita K, Nakagaki H (1995) Application of semi-automatic robot technology on hot-line maintenance work. In Proceedings of 1995 IEEE International Conference on Robotics and Automation, 21–27 May 1995, vol 1, pp 843–850 vol 1. https://doi.org/10.1109/ROBOT.1995.525388

  29. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  MathSciNet  Google Scholar 

  30. Park S, Moon B, Ko S, Yu S, Paik J (2017) Low-light image restoration using bright channel prior-based variational Retinex model. EURASIP J Image Video Process 2017(1):1–11

    Article  Google Scholar 

  31. Peng L, Huang Y, Kunlun Y (2018) Multi-algorithm fusion of RGB and HSV color spaces for image enhancement. In 2018 37th Chinese Control Conference (CCC): IEEE, pp 9584–9589

  32. Petro AB, Sbert C, Morel J-M (2014) Multiscale retinex. Image Processing On Line pp 71–88

  33. Pizer SM et al (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  34. Qiu Y, Tang L, Li B, Niu S, Niu T (2020) Uneven illumination surface defects inspection based on saliency detection and intrinsic image decomposition. IEEE Access 8:190663–190676. https://doi.org/10.1109/ACCESS.2020.3032108

    Article  Google Scholar 

  35. Rahman Z-U, Jobson DJ, Woodell GA (1996) Multi-scale retinex for color image enhancement. In Proceedings of 3rd IEEE International Conference on Image Processing, vol 3: IEEE, pp 1003–1006

  36. Ren W et al (2018) Gated fusion network for single image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3253–3261

  37. Santoso AJ, Nugroho LE, Suparta GB, Hidayat R (2011) Compression ratio and peak signal to noise ratio in grayscale image compression using wavelet. Int J Comput Sci Technol 2(2):7–11

    Google Scholar 

  38. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Commun Rev 5(1):3–55

    Article  MathSciNet  Google Scholar 

  39. Shouyin L, Yanping L, Wei Q (2009) Robotic live-working for electric power lines maintenances. In 2009 4th IEEE Conference on Industrial Electronics and Applications: IEEE pp 1716–1719

  40. Son T, Kang J, Kim N, Cho S, Kwak S (2020) URIE: universal image enhancement for visual recognition in the wild. In Computer Vision – ECCV 2020, Cham, A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds., 2020: Springer International Publishing, pp 749–765

  41. Takaoka K, Yokoyama K, Wakisako H, Yano K, Higashijima K, Murakami S (2001) Development of the fully-automatic live-line maintenance robot-Phase III. In Proceedings of the 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001). Assembly and Disassembly in the Twenty-first Century. (Cat. No. 01TH8560): IEEE, pp 423–428

  42. Tang M, Gu Y, Wang S, Liang Q (2019) DCBot: an autonomous hot-line working robot for 110 kV substation. Robot Autonomous Syst 119:247–262. https://doi.org/10.1016/j.robot.2019.07.008

    Article  Google Scholar 

  43. Tang M, Gu Y, Zhang Y, Wang S (2017) Dual manipulator system of the field hot-line working robot in 110-kV substations. Ind Robot Int J 44(4):479–490. https://doi.org/10.1108/IR-11-2016-0320

    Article  Google Scholar 

  44. Triantafyllidou D, Moran S, McDonagh S, Parisot S, Slabaugh G (2020) Low light video enhancement using synthetic data produced with an intermediate domain mapping. In European Conference on Computer Vision Springer, pp 103–119

  45. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  46. Wang Y-K, Huang W-B (2014) A CUDA-enabled parallel algorithm for accelerating retinex. J Real Time Image Process 9(3):407–425

    Article  Google Scholar 

  47. Wang W, Li B, Zheng J, Xian S, Wang J (2008) A fast multi-scale retinex algorithm for color image enhancement. In 2008 International Conference on Wavelet Analysis and Pattern Recognition, vol 1: IEEE, pp 80–85

  48. Wang W, Yuan X, Chen Z, Wu X, Gao Z (2021) Weak-light image enhancement method based on adaptive local gamma transform and color compensation. J Sens 2021:5563698. https://doi.org/10.1155/2021/5563698

    Article  Google Scholar 

  49. Wang S, Zheng J, Hu H-M, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process 22(9):3538–3548

    Article  Google Scholar 

  50. Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560. https://doi.org/10.48550/arXiv.1808.04560

  51. Xiao W, Tang Z, Yang C, Liang W, Hsieh M-Y (2022) ASM-VoFDehaze: a real-time defogging method of zinc froth image. Connect Sci 34(1):709–731. https://doi.org/10.1080/09540091.2022.2038543

    Article  Google Scholar 

  52. Yang W, Wang S, Fang Y, Wang Y, Liu J (2020) From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3063–3072

  53. Zhan Y, Zhang G (2019) An improved OTSU algorithm using histogram accumulation moment for ore segmentation. Symmetry 11(3):431 https://doi.org/10.3390/sym11030431

  54. Zhang L et al (2016) Simultaneous enhancement and noise reduction of a single low-light image. IET Image Process 10(11):840–847

    Article  Google Scholar 

  55. Zhao B, Gong X, Wang J, Zhao L (2021) Low-light image enhancement based on multi-path interaction. Sensors 21(15):4986

    Article  Google Scholar 

  56. Zhao L, Liu Z-L, Ye L, Wei G, Xiang T-C, Jing L (2019) Trajectory Planning and Simulation for Live-Working Robot. DEStech Transactions on Engineering and Technology Research, no. amsms https://doi.org/10.12783/dtetr/amsms2019/31870

  57. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graph Gems pp 474–485

Download references

Funding

The work is supported by the National Key R&D Program of China (2018YFB1307400).

Author information

Authors and Affiliations

  1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China

    Yuze Qiu, Yutao Chen, Yuxiang Zheng, Yahao Wang & Erbao Dong

  2. State Grid Anhui Electric Power Company Electric Power Research Institute, Hefei, 230601, China

    Kai Wu & Shaolei Wu

  3. State Grid Intelligent Technology Co., Ltd, Jinan, 250001, China

    Rui Guo & Yuliang Zhao

Authors
  1. Yuze Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yutao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuxiang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yahao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shaolei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuliang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Erbao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erbao Dong.

Ethics declarations

Conflict of interests

The authors declared that they have no conflicts of interest to this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, Y., Chen, Y., Zheng, Y. et al. Adaptive uneven illumination correction method for autonomous live-line maintenance robot. Multimed Tools Appl 82, 23453–23481 (2023). https://doi.org/10.1007/s11042-022-14249-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14249-1

Keywords

Search

Navigation