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Progressive Complementary Knowledge Aggregation for CdZnTe Defect Segmentation
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AI
DOI：10.1109/TII.2024.3384517.png)
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Abstract
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Automatic quality inspection of industrial products is an indispensable part of modern manufacturing. Cadmium zinc telluride (CdZnTe) crystal is an important industrial raw material, but the special photosensitive properties of CdZnTe make it show different defect boundaries under different lighting angles, which poses challenges for quality inspection. In this article, we propose progressive complementary knowledge aggregation (PCKA) for CdZnTe defect segmentation, which is model-agnostic. First, the 12 images of CdZnTe crystal with different lighting angles are fed into the preliminary aggregation net to aggregate unique pixel-level clues. Second, we use a latent aggregation net to acquire the feature-level complementary clues under the guidance of the pixel-level clues within latent space. Such a learning paradigm is an effective solution for the special photosensitive properties of CdZnTe crystal. Extensive experiments on self-collected dataset demonstrate the effectiveness and efficiency of our PCKA compared with other solutions.
Keywords:
Computer vision (CV)
deep learning
defect segmentation
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