Surface Defect Saliency of Magnetic Tile

被引：0

作者：

Huang, Yibin ^{[1
]}

Qiu, Congying ^{[2
]}

Guo, Yue ^{[3
]}

Wang, Xiaonan ^{[3
]}

Yuan, Kui ^{[3
]}

机构：

[1] Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China

[2] Columbia Univ, Civil Engn & Engn Mech Dept, 500 West 120th St, New York, NY 10027 USA

[3] Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China

来源：

2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | 2018年

基金：

中国国家自然科学基金;

关键词：

OBJECT DETECTION; IMAGES; STRIP;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Vision-based detection on surface defects has long postulated in the magnetic tile automation process. In this work, we introduce a real-time and multi-module neural network model called MCuePush U-Net, specifically designed for the image saliency detection of magnetic tile. We show that the model exceeds the state-of-the-art, in which it both effectively and explicitly maps multiple surface defects from low-contrast images. Our model significantly reduces time cost of machinery from 0.5s per image to 0.07s, and enhances saliency accuracy on surface defect detection.

引用

页码：612 / 617

页数：6

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