为保证反应堆的安全运行,需要采用多种检测技术确保燃料芯块质量。针对燃料芯块表面裂纹检测中因图像对比度低、背景复杂而导致的裂纹误检率高的问题,提出了一种基于卷积神经网络(CNN)和Beamlet算法相结合的表面裂纹检测算法。对图像进行等尺度分割作为裂纹识别模型(CrackCNN)的训练和测试样本;采用训练完成的CrackCNN对图像中含裂纹的区域进行识别和定位;采用Beamlet算法针对含裂纹区域进行裂纹检测。该算法将CNN和Beamlet相结合,充分发挥两者的优势,有效降低了裂纹误检概率,提高了检测精度。实验结果表明,相对于单独采用Beamlet算法,本文算法的F-measure提升了6.4%;相对于双重阈值和张量投票算法,本文算法的F-measure提升了3.4%。

To ensure safe reactor operation, a variety of detection techniques are required to ensure the qualities of fuel pellets. To address high misdetection rate of cracks due to low contrast and complex background in the detection of surface cracks in fuel pellets, a surface crack detection algorithm based on convolutional neural networks (CNN) and the Beamlet algorithm is proposed. First, images are divided into equal-sized patches, which are used as training samples for the crack recognition model (CrackCNN). Then, the crack-containing region in the image is identified and located by the trained CrackCNN. Finally, a crack in identified region is detected by the Beamlet algorithm. The proposed method, which utilizes both CNNs and Beamlet, can improve detection accuracy and effectively reduce the probability of crack misdetection. Experimental results demonstrate that the F-measure of the proposed algorithm is enhanced by 6.4% and 3.4% compared to using only the Beamlet algorithm and using the double threshold and tensor voting algorithm, respectively.