Investigation into defect image segmentation algorithms for galvanised steel sheets under texture backgrounds

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION Insight Pub Date : 2023-09-01 DOI:10.1784/insi.2023.65.9.492
Rui Pan, Wei Gao, Yunbo Zuo, Guoxin Wu, Yuda Chen
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Abstract

Image segmentation is a significant step in image analysis and computer vision. Many entropy-based approaches have been presented on this topic. Among them, Tsallis entropy is one of the best-performing methods. In this paper, the surface defect images of galvanised steel sheets were studied. A two-dimensional asymmetric Tsallis cross-entropy image segmentation algorithm based on chaotic bee colony algorithm optimisation was used to investigate the segmentation of surface defects under complex texture backgrounds. On the basis of Tsallis entropy threshold segmentation, a more concise expression form was used to define the asymmetric Tsallis cross-entropy in order to reduce the calculation complexity of the algorithm. The chaotic algorithm was combined with the artificial bee colony (ABC) algorithm to construct the chaotic bee colony algorithm, so that the optimal threshold of Tsallis entropy could be quickly identified. The experimental results showed that compared with the maximum Shannon entropy algorithm, the calculation time of this algorithm decreased by about 58% and the threshold value increased by about (26%, 54%). Compared with the two-dimensional Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 35% and about 20% was improved in the g-axis direction only. Compared with the two-dimensional asymmetric Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 30% and the threshold values of the two algorithms were almost the same. The algorithm proposed in this paper can rapidly and effectively segment defect targets, making it a more suitable method for detecting surface defects in factories with a rapid production pace.
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纹理背景下镀锌钢板缺陷图像分割算法研究
图像分割是图像分析和计算机视觉中的一个重要步骤。关于这个主题已经提出了许多基于熵的方法。其中,Tsallis熵是性能最好的方法之一。本文对镀锌钢板表面缺陷图像进行了研究。采用基于混沌蜂群算法优化的二维非对称Tsallis交叉熵图像分割算法,研究了复杂纹理背景下表面缺陷的分割问题。为了降低算法的计算复杂度,在Tsallis熵阈值分割的基础上,采用更简洁的表达形式来定义非对称Tsallis交叉熵。将混沌算法与人工蜂群(artificial bee colony, ABC)算法相结合,构造混沌蜂群算法,从而快速识别出Tsallis熵的最优阈值。实验结果表明,与最大香农熵算法相比,该算法的计算时间缩短了约58%,阈值提高了约(26%,54%)。与二维Tsallis交叉熵算法相比,该算法的计算时间缩短了约35%,仅在g轴方向上提高了约20%。与二维非对称Tsallis交叉熵算法相比,该算法的计算时间缩短了约30%,两种算法的阈值基本相同。本文提出的算法可以快速有效地分割缺陷目标,使其更适合于快速生产节奏工厂的表面缺陷检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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