Infrared Image Segmentation of Aircraft Skin Based on Otsu and Improved I-Ching Divination Evolutionary Algorithm

doi:10.16182/j.issn1004731x.joss.19-0455

Abstract

Abstract: Infrared thermal imaging non-destructive testing is one of the commonly used methods for aircraft skin detection. Aiming at Otsu's large computational complexity and poor real-time performance, an aircraft skin infrared image segmentation method based on Otsu and an improved I-Ching divination evolutionary algorithm (IDEA) is proposed. The roulette selection operator is improved by using roulette selection for the I-Ching map of state size 3n, from which the n individuals with the maximum fitness values are then selected as new populations. The experimental results show that the proposed algorithm is superior to several other improved optimization algorithms both in terms of convergence speed and time consumption, which indicates that the algorithm can effectively improve the speed of threshold solution.

Key words: maximum between-class variance, infrared image, image segmentation, I-Ching divination evolutionary algorithm, I-Ching operators

CLC Number:

TP391.9

Wang Kun, Ji Yao, Liu Peilun, Wang Li. Infrared Image Segmentation of Aircraft Skin Based on Otsu and Improved I-Ching Divination Evolutionary Algorithm[J]. Journal of System Simulation, 2021, 33(2): 421-433.

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