CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Surface defect saliency of magnetic tile
Yibin Huang1; Congying qiu; Kui yuan1
Source PublicationThe visual computer

Computer vision builds a connection between image processing and industrials, bringing modern perception to the automated  manufacture of magnetic tiles. In this article, we propose a real-time model called MCuePush U-Net, specifically designed for saliency detection of surface defect. Our model consists of three main components: MCue, U-Net and Push network. MCue generates three-channel resized inputs, including one MCue saliency image and two raw images; U-Net learns the most informative regions, and essentially it is a deep hierarchical structured convolutional network; Push network defines the specific location of predicted surface defects with bounding boxes, constructed by two fully connected layers and one output layer. We show that the model exceeds the state of the art in saliency detection of magnetic tiles, in which it both effectively and explicitly maps multiple surface defects from low-contrast images. The proposed model significantly reduces time cost  of machinery from 0.5s per image to 0.07s and enhances detection accuracy for image-based defect examinations.

KeywordSaliency Surface Defect Inspection
Indexed BySCI
WOS IDWOS:000511966800008
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Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorYibin Huang
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yibin Huang,Congying qiu,Kui yuan. Surface defect saliency of magnetic tile[J]. The visual computer,2018,34(8):1-12.
APA Yibin Huang,Congying qiu,&Kui yuan.(2018).Surface defect saliency of magnetic tile.The visual computer,34(8),1-12.
MLA Yibin Huang,et al."Surface defect saliency of magnetic tile".The visual computer 34.8(2018):1-12.
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