Defect Detection Image Processing Technology Based on Swarm Intelligence Optimization Algorithm

The swarm intelligence optimization algorithm has obtained good results in practical application in the field of image processing with defect detection, and it has become the focus and hot spot of attention and research in the field of image processing. In this paper, the application of ALO as the r...

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Vydané v:Journal of physics. Conference series Ročník 2400; číslo 1; s. 12031 - 12037
Hlavní autori: Zhang, Kui, Zhu, Shan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.12.2022
Predmet:
Algorithms
Defects
Image enhancement
Image processing
Optimization
Optimization algorithms
Perturbation
Physics
Swarm intelligence
ISSN:1742-6588, 1742-6596
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Shrnutí:The swarm intelligence optimization algorithm has obtained good results in practical application in the field of image processing with defect detection, and it has become the focus and hot spot of attention and research in the field of image processing. In this paper, the application of ALO as the representative of the relevant swarm intelligence optimization algorithm is studied to address the problems and shortcomings of image processing technology in the field of object defect detection. By extracting typical defect detection image samples, the effect of the application of the algorithm in sample processing is systematically studied. In addition, the introduction of perturbation strategy and inertia weights in ALO effectively improves the search performance of the algorithm. Finally, this paper analyzes the performance comparison between the commonly used defect detection image processing techniques and the algorithm in this paper by establishing comparative verification experiments. The experimental results show that the image processing strategy constructed in this paper has significant application advantages in the dimensions of image enhancement and image processing applicability.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2400/1/012031