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The appearance quality of nuclear fuel pellets is closely associated with the operational safety and efficiency of nuclear reactors, and must exhibit high quality before being loaded into reactors. Currently, visual inspection by human operators is employed to control the quality of nuclear fuel pellets. However, manual inspection is highly subjective, prone to visual fatigue, carries risks of false and missed detections, and lacks the capability for quantitative defect analysis. To effectively detect surface defects of UO₂ nuclear fuel pellets, an automated inspection system based on machine vision has been designed. The system adopts a modular architecture: first, preprocessing the acquired pellet images; then, utilizing image segmentation techniques tailored to the surface characteristics of the pellets to extract defect regions; subsequently, applying connectivity analysis for defect examination; and finally, employing a combined approach of Support Vector Machine (SVM) classifier and Convolutional Neural Network (CNN) for defect classification, enabling automatic identification of pellet surface defects. The results demonstrate that the system achieves high detection accuracy and efficiency, meeting the requirements of industrial automated inspection.

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核燃料芯块的外观质量关乎反应堆的运行安全与效率，在投入使用时必须满足高质量要求。目前，主要采用人工目视方式控制核燃料芯块外观质量，该方式主观经验性强、容易视觉疲劳，存在误检漏检风险，且无法对缺陷进行定量分析。为了高效进行UO₂核燃料芯块外观缺陷检测，设计了一种基于机器视觉的UO₂芯块外观缺陷自动检测系统。该系统采用模块化设计，先对采集到的芯块图像进行预处理；再针对芯块表面特征，通过图像分割技术提取缺陷区域，依托连通性分析方法进行缺陷分析；最后使用SVM分类器与卷积神经网络（CNN）相结合的方法对缺陷进行分类，实现芯块外观缺陷自动识别。结果表明，该系统具有较好的检测精度和检测效率，满足工业自动化检测需求。

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