干旱区生态环境知识资源中心(Arid): OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection

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DOI	10.3390/math10214114
	OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection
	Hong, Younggi; Yoo, Seok Bong
通讯作者	Yoo, SB
来源期刊	MATHEMATICS
EISSN	2227-7390
出版年	2022
卷号	10 期号:21
英文摘要	Surface defect detection systems, which have advanced beyond conventional defect detection methods, lower the risk of accidents and increase working efficiency and productivity. Most fault detection techniques demand extra tools, such as ultrasonic sensors or lasers. With the advancements, these techniques can be examined without additional tools. We propose a morphological attention ensemble learning for surface defect detection called OASIS-Net, which can detect defects of three kinds (crack, efflorescence, and spalling) at the bounding box level. Based on the morphological analysis of each defect, OASIS-Net offers specialized loss functions for each defect that can be examined. Specifically, high-frequency image augmentation, connectivity attention, and penalty areas are used to detect cracks. It also compares the colors of the sensing objects and analyzes the image histogram peaks to improve the efflorescence-verification accuracy. Analyzing the ratio of the major and minor axes of the spalling through morphological comparison reveals that the spalling-detection accuracy improved. Defect images are challenging to obtain due to their properties. We labeled some data provided by AI hub and some concrete crack datasets and used them as custom datasets. Finally, an ensemble learning technique based on multi-task classification is suggested to learn and apply the specialized loss of each class to the model. For the custom dataset, the accuracy of the crack detection increased by 5%, the accuracy of the efflorescence detection increased by 4.4%, and the accuracy of the spalling detection increased by 6.6%. The experimental results reveal that the proposed network outperforms the previous state-of-the-art methods.
英文关键词	surface defect detection morphological attention ensemble learning crack efflorescence spalling
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000885812700001
WOS关键词	CONCRETE DETERIORATION ; STEEL SURFACE ; CLASSIFICATION ; SEGMENTATION
WOS类目	Mathematics
WOS研究方向	Mathematics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393767
推荐引用方式 GB/T 7714	Hong, Younggi,Yoo, Seok Bong. OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection[J],2022,10(21).
APA	Hong, Younggi,&Yoo, Seok Bong.(2022).OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection.MATHEMATICS,10(21).
MLA	Hong, Younggi,et al."OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection".MATHEMATICS 10.21(2022).