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• W4367396929 abstract "The storage and labeling of industrial data incur significant costs during the development of defect detection algorithms. Active learning solves these problems by selecting the most informative data among the given unlabeled data. The existing active learning methods for image segmentation focus on studying natural images and medical images, with less attention given to industrial images, and little research has been performed on imbalanced data. To solve these problems, we propose an active learning framework to selecting informative data for defect segmentation under imbalanced data. In the initialization stage, the framework uses self-supervised learning to initialize the data so that the initialization data contain more defect data, thereby solving the cold-start problem. During the iterative stage, we design the main body of the active learning framework, which is composed of a segmentation learner and a reconstruction learner. These learners use supervised learning to further improve the framework’s ability to select informative data. The experimental results obtained on public and self-owned datasets show that the framework can save 70% of the required storage space and greatly reduce the cost of labeling. The intersection over union value proves that the designed framework can achieve the equivalent effect of labeling the whole dataset by labeling partial data." @default.
• W4367396929 created "2023-04-30" @default.
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• W4367396929 date "2023-04-01" @default.
• W4367396929 modified "2023-09-24" @default.
• W4367396929 title "Selecting informative data for defect segmentation from imbalanced datasets via active learning" @default.
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• W4367396929 doi "https://doi.org/10.1016/j.aei.2023.101933" @default.
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