期刊
SENSORS
卷 20, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/s20061650
关键词
surface defect detection; active learning; deep learning
资金
- National Key Research and Development Plan [2017YFF0204800]
- National Natural Science Foundations of China [61971307, 61905175, 51775377]
- National Science and Technology Major Project [2017-V-0009]
- Tianjin Natural Science Foundations of China [17JCQNJC01100]
- Equipment Pre-Research Field Fund [61405180505, 61400040303]
- Fok Ying Tung Education Foundation [171055]
- Science and Technology on Underwater Information and Control Laboratory [6142218081811]
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.
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