To address the challenge of defect detection in tempered glass panel production rising from sample scarcity, this paper proposes a few-shot detection methodology that integrates an enhanced Stable Diffusion model with Mask R-CNN. Specifically, the approach utilizes a Mask Encoder to optimize the Stable Diffusion architecture, employing the Structural Similarity Index Measure (SSIM) to evaluate sample quality. This process generates high-fidelity virtual samples to construct a hybrid dataset for training data augmentation. Furthermore, a resource isolation strategy is adopted to facilitate online detection using an improved semi-supervised Mask R-CNN framework. Experimental results demonstrate that the proposed scheme effectively resolves detection difficulties for eight defect types, including edge chipping and scratches. The method achieves an mAP50 of 81.5%, representing a nearly 47% improvement over baseline methods relying solely on real samples, thereby realizing high-precision and high-efficiency industrial defect detection.
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Kai Hou
Jing-Fang Yang
Peng Zhang
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SHILAP Revista de lepidopterología
Qilu University of Technology
Shandong Academy of Sciences
Taishan University
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Hou et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69a75d41c6e9836116a26fbe — DOI: https://doi.org/10.3390/info17020122