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Open AccessArticle

Deep Active Learning for Surface Defect Detection

The State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1650;
Received: 14 February 2020 / Revised: 12 March 2020 / Accepted: 13 March 2020 / Published: 16 March 2020
Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data. View Full-Text
Keywords: surface defect detection; active learning; deep learning surface defect detection; active learning; deep learning
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Lv, X.; Duan, F.; Jiang, J.-J.; Fu, X.; Gan, L. Deep Active Learning for Surface Defect Detection. Sensors 2020, 20, 1650.

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