Surface Defect Saliency of Magnetic Tile

24 Aug 2018  ·  Yibin Huang, Congying Qiu, Yue Guo, Xiaonan Wang, and Kui Yuan ·

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.



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Results from the Paper

 Ranked #1 on Anomaly Detection on Surface Defect Saliency of Magnetic Tile (Segmentation AUROC metric, using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Anomaly Detection Surface Defect Saliency of Magnetic Tile MCuePush (supervised) Segmentation AUROC 98.5 # 1


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