FABRIC DEFECT DETECTION METHOD BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK AND VISUAL SALIENCY

WIPO Patent Application WO/2019/104767

A1

A fabric defect detection method based on a deep convolutional neural network and visual saliency, wherein same falls within the technical field of image processing. The method comprises carrying out processing based on a defect region positioning module and a defect semantic segmentation module, wherein the defect region positioning module uses two deep learning models, i.e. a local convolutional neural network and a global convolutional neural network, for fusion, automatically extracts advanced features of a fabric defect and applies same to a defect image, and obtains precise positioning of a defect region; and the defect semantic segmentation module uses a positioning result of the defect region, and in conjunction with a super pixel image segmentation method based on visual saliency, acquires a defect priori foreground point and precisely segments a defect target, and finally realizes defect detection. The method has good adaptability to fabric images and a high precision, and can effectively detect a defect in the fabric image under the conditions of a complex background and noise interference.

LI QINGWU (CN)
XING JUN (CN)
MA YUNPENG (CN)
ZHOU YAQIN (CN)
WU CHENHUI (CN)

PCT/CN2017/116837

June 06, 2019

December 18, 2017

Click for automatic bibliography generation  

CHANGZHOU CAMPUS OF HOHAI UNIV (CN)

G06K9/62

CN105701508A	2016-06-22
CN105701477A	2016-06-22
CN106599830A	2017-04-26
US20120237081A1	2012-09-20

NANJING ZONGHENG INTELLECTUAL PROPERTY AGENCY CO., LTD. (CN)

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