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Minimal Kapur cross-entropy-based image segmentation for distribution grid inspection using improved INFO optimization algorithm

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Abstract

Distribution grid network has problems such as long mileage, large scale, complex surrounding environment, and aging of equipment. It is the development trend of power distribution network operation and maintenance to use unmanned aerial vehicles to patrol and combine with image processing technology for intelligent detection of equipment status. Image segmentation is well-known technique for extracting defect regions of equipment from distribution network inspection images. Therefore, this paper proposes an efficient a novel multilevel thresholding segmentation method to improve the fault diagnosis process with an improved weighted mean of vectors optimization (IINFO) algorithm. The IINFO algorithm adopts various measures to improve the optimization results, including Gaussian mutation to increase the local search ability and range of the optimal individual, Cauchy mutation to enhance the global search ability of its vector individual, reflective learning operators to strengthen self-learning and avoid local optimal solutions, and parallel operation to improve the utilization of computational resources. Moreover, two-dimensional Kapur cross-entropy is used as an objective function to solve the multilevel thresholding problem. The proposed method is evaluated using benchmark functions and distribution network inspection image datasets and is compared with 12 other metaheuristic algorithms. The results demonstrate that the proposed method has better performance and a higher ability to find optimal solutions compared to the other algorithms. These findings suggest that our method may be useful in improving the accuracy and efficiency of distribution network inspections and have significant potential for practical applications.

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References

  1. Yu H, Song J, Chen C, Heidari AA, Liu J, Chen H, Zaguia A, Mafarja M (2022) Image segmentation of leaf spot diseases on maize using multi-stage Cauchy-enabled grey wolf algorithm. Eng Appl Artif Intell 109:1. https://doi.org/10.1016/j.engappai.2021.104653

    Article  Google Scholar 

  2. Ben Ishak A (2017) Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework. Physica A 466:521–536. https://doi.org/10.1016/j.physa.2016.09.053

    Article  Google Scholar 

  3. Zhao S, Wang P, Heidari AA, Chen H, Turabieh H, Mafarja M, Li C (2021) Multilevel threshold image segmentation with diffusion association slime Mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 134:104427. https://doi.org/10.1016/j.compbiomed.2021.104427

    Article  Google Scholar 

  4. Elaziz MA (2021) A multi-leader whale optimization algorithm for global optimization and image segmentation. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.114841

  5. Mousavirad SJ, Oliva D, Chakrabortty RK, Zabihzadeh D, Hinojosa S (2022) Population-based self-adaptive generalised masi entropy for image segmentation: a novel representation. Knowl-Based Syst 245:1. https://doi.org/10.1016/j.knosys.2022.108610

    Article  Google Scholar 

  6. Chen Y, Wang M, Heidari AA, Shi B, Hu Z, Zhang Q, Chen H, Mafarja M, Turabieh H (2022) Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Syst Appl 194:1. https://doi.org/10.1016/j.eswa.2022.116511

    Article  Google Scholar 

  7. Ray S, Parai S, Das A, Dhal KG, Naskar PK (2021) Cuckoo search with differential evolution mutation and masi entropy for multi-level image segmentation. Multimed Tools Appl 81(3):4073–4117. https://doi.org/10.1007/s11042-021-11633-1

    Article  Google Scholar 

  8. Bao X, Jia H, Lang C (2019) A novel hybrid Harris Hawks optimization for color image multilevel thresholding segmentation. IEEE Access 7:76529–76546

    Article  Google Scholar 

  9. Ji X, Henriques JF, Vedaldi A (2019) Invariant information clustering for unsupervised image classification and segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 9865–9874

  10. Bai X, Wang Y, Liu H, Guo S (2017) Symmetry information based fuzzy clustering for infrared pedestrian segmentation. IEEE Trans Fuzzy Syst 26(4):1946–1959

    Article  Google Scholar 

  11. Araki S, Nomura H, Wakami N Segmentation of thermal images using the fuzzy c-means algorithm. In: [Proceedings 1993] 2nd IEEE International Conference on Fuzzy Systems. IEEE, pp 719–724

  12. Wang Y, Bai X (2018) Intensity inhomogeneity suppressed fuzzy c-means for infrared pedestrian segmentation. IEEE Trans Intell Transp Syst 20(9):3361–3374

    Article  Google Scholar 

  13. Shakeel PM, Baskar S, Sampath R, Jaber MM (2019) Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (fmed). Int J Signal Imag Syst Eng 11(5):270–278

    Google Scholar 

  14. Yu X, Qi Y, Lu Z, Hu N (2013) Implicit active contours driven by local and global image fitting energy for image segmentation and target localization. J Sens 2013:1

    Article  Google Scholar 

  15. Zhao D, Liu L, Yu F, Heidari AA, Wang M, Liang G, Muhammad K, Chen H (2021) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2d Kapur entropy. Knowl-Based Syst 216:1. https://doi.org/10.1016/j.knosys.2020.106510

    Article  Google Scholar 

  16. Xu F, Liu X, Fujimura K (2005) Pedestrian detection and tracking with night vision. IEEE Trans Intell Transp Syst 6(1):63–71

    Article  Google Scholar 

  17. Tang Q, Gao S, Liu Y, Yu F (2019) Infrared image segmentation algorithm for defect detection based on FODPSO. Infrared Phys Technol 102:103051

    Article  Google Scholar 

  18. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Machine Intell

  19. Lang C, Cheng G, Tu B, Li C, Han J (2023) Base and meta: a new perspective on few-shot segmentation. IEEE Trans Pattern Anal Mach Intell

  20. Cheng G, Lang C, Han J (2022) Holistic prototype activation for few-shot segmentation. IEEE Trans Pattern Anal Mach Intell 45(4):4650–4666

    Google Scholar 

  21. Lang C, Cheng G, Tu B, Han J (2022) Learning what not to segment: A new perspective on few-shot segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8057–8067

  22. Lang C, Wang J, Cheng G, Tu B, Han J (2023) Progressive parsing and commonality distillation for few-shot remote sensing segmentation. IEEE Trans Geosci Remote Sens

  23. Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Comput Sci 65:797–806

    Article  Google Scholar 

  24. Houssein EH, Helmy BE-D, Oliva D, Jangir P, Premkumar M, Elngar AA, Shaban H, (2022) An efficient multi-thresholding based Covid-19 ct images segmentation approach using an improved equilibrium optimizer. Biomed Signal Process Control 73:1. https://doi.org/10.1016/j.bspc.2021.103401

  25. Jia H, Sun K, Song W, Peng X, Lang C, Li Y (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using masi entropy. IEEE Access 7:134448–134474

    Article  Google Scholar 

  26. Abutaleb AS (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput Vis Gr Image Process 47(1):22–32

    Article  Google Scholar 

  27. Xiao Y, Cao Z, Yuan J (2014) Entropic image thresholding based on GLGM histogram. Pattern Recogn Lett 40:47–55. https://doi.org/10.1016/j.patrec.2013.12.017

    Article  Google Scholar 

  28. Zheng X, Ye H, Tang Y (2017) Image bi-level thresholding based on gray level-local variance histogram. Entropy 19(5):1. https://doi.org/10.3390/e19050191

    Article  MathSciNet  Google Scholar 

  29. Buades A, Coll B, Morel J-M A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 2. IEEE, pp. 60–65

  30. Borjigin S, Sahoo PK (2019) Color image segmentation based on multi-level Tsallis–Havrda–Charvát entropy and 2d histogram using PSO algorithms. Pattern Recogn 92:107–118. https://doi.org/10.1016/j.patcog.2019.03.011

    Article  Google Scholar 

  31. Ren L, Heidari AA, Cai Z, Shao Q, Liang G, Chen H-L, Pan Z (2022) Gaussian kernel probability-driven slime mould algorithm with new movement mechanism for multi-level image segmentation. Measurement 192:1. https://doi.org/10.1016/j.measurement.2022.110884

    Article  Google Scholar 

  32. Chouksey M, Jha RK (2021) A multiverse optimization based colour image segmentation using variational mode decomposition. Expert Syst Appl 171:1. https://doi.org/10.1016/j.eswa.2021.114587

    Article  Google Scholar 

  33. Houssein EH, Hussain K, Abualigah L, Elaziz MA, Alomoush W, Dhiman G, Djenouri Y, Cuevas E (2021) An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowl-Based Syst 229:1. https://doi.org/10.1016/j.knosys.2021.107348

    Article  Google Scholar 

  34. Bandyopadhyay R, Kundu R, Oliva D, Sarkar R (2021) Segmentation of brain MRI using an altruistic Harris Hawks’ optimization algorithm. Knowl-Based Syst 232:1. https://doi.org/10.1016/j.knosys.2021.107468

    Article  Google Scholar 

  35. Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH (2022) Info: an efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 195:1. https://doi.org/10.1016/j.eswa.2022.116516

    Article  Google Scholar 

  36. Feng Z-k, Niu W-j, Liu S (2021) Cooperation search algorithm: a novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl Soft Comput 98:1. https://doi.org/10.1016/j.asoc.2020.106734

    Article  Google Scholar 

  37. Sankur B (2002) Statistical evaluation of image quality measures. J Electron Imag 11(2):1. https://doi.org/10.1117/1.1455011

    Article  Google Scholar 

  38. Aja-Fernandez S, Estepar RSJ, Alberola-Lopez C, Westin C-F Image quality assessment based on local variance. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 4815–4818

  39. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–86. https://doi.org/10.1109/TIP.2011.2109730

    Article  MathSciNet  Google Scholar 

  40. Reisenhofer R, Bosse S, Kutyniok G, Wiegand T (2018) A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Process: Image Commun 61:33–43. https://doi.org/10.1016/j.image.2017.11.001

    Article  Google Scholar 

  41. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064

    Article  Google Scholar 

  42. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  43. Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Gr Image Process 29(3):273–285

    Article  Google Scholar 

  44. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  45. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international Conference on Neural Networks, vol 4. IEEE, pp 1942–1948

  46. Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110. https://doi.org/10.1016/j.matcom.2021.08.013

    Article  MathSciNet  Google Scholar 

  47. Salawudeen AT, Mu’azu MB, Sha’aban YA, Adedokun AE (2021) A novel smell agent optimization (SAO): An extensive CEC study and engineering application. Knowl-Based Syst 232:1. https://doi.org/10.1016/j.knosys.2021.107486

    Article  Google Scholar 

  48. Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl-Based Syst 242:1. https://doi.org/10.1016/j.knosys.2022.108320

    Article  Google Scholar 

  49. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  50. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  51. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  52. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  53. Civicioglu P, Besdok E, Gunen MA, Atasever UH (2020) Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Comput Appl 32:3923–3937

    Article  Google Scholar 

  54. Naik MK, Panda R, Wunnava A, Jena B, Abraham A (2021) A leader Harris Hawks optimization for 2-d masi entropy-based multilevel image thresholding. Multimed Tools Appl 80(28–29):35543–35583. https://doi.org/10.1007/s11042-020-10467-7

    Article  Google Scholar 

  55. Zhang J, Hu J Image segmentation based on 2d otsu method with histogram analysis. In: 2008 International Conference on Computer Science and Software Engineering, vol. 6. IEEE, pp 105–108

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Funding

This work was supported in part by the Hunan Provincial Natural Science Foundation of China under Grant Nos. 2021JJ30732, the Young Teachers Program of Changsha University of Science & Technology under Grant No. 2019QJCZ041 and 2019QJCZ079. The authors gratefully appreciate this support.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Changsha University of Science & Technology, Tianxin Subdistrict, Changsha, 410114, China

    Junjun Jiao, Zhisheng Chen & Tao Zhou

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  1. Junjun Jiao
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  2. Zhisheng Chen
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  3. Tao Zhou
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Contributions

JJ was involved in Methodology, Software, Formal analysis, and Writing—original draft. ZC contributed to Conceptualization, Methodology, Resources, Formal analysis, and Writing—review and editing. TZ was involved in Methodology and Formal analysis.

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Correspondence to Zhisheng Chen.

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Jiao, J., Chen, Z. & Zhou, T. Minimal Kapur cross-entropy-based image segmentation for distribution grid inspection using improved INFO optimization algorithm. J Supercomput 80, 4309–4352 (2024). https://doi.org/10.1007/s11227-023-05628-y

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