先验知识辅助的金属涂层损伤分割方法

Knowledge-Driven Metal Coating Defect Segmentation

  • 摘要: 金属涂层损伤的自动识别是一项具有重大实际应用价值的研究。随着深度学习在各类材质的表面损伤图像分割任务中取得突破性进展,大部分研究工作使用端到端的深度卷积神经网络分割模型分割损伤区域。然而,端到端深度学习方法很难利用金属涂层损伤相关的先验知识来识别损伤尺度差异大、训练数据不足等问题。因此,设计了一种基于先验知识的金属涂层损伤区域分割算法,结合深度学习分割模型U-Net实现金属涂层损伤的自动识别。该算法基于Hue通道分布和边缘响应对金属涂层图像中的异常区域进行分割,深度学习分割模型利用先验知识排除异常样本,可有效避免过拟合。在含有开裂、起泡、生锈和脱落这4类损伤的金属涂层数据集中,该算法取得了81.24% mIoU的分割效果,优于端到端的深度学习方法。实验结果表明,先验知识辅助能够有效提升深度学习分割模型对金属涂层损伤的分割效果。

     

    Abstract: Automatic recognition of metal coating defects has significant value in realistic applications. As deep learning makes breakthrough in surface defect segmentation for a variety of materials, most of deep convolutional neural network segmentation models are trained in an end-to-end manner. However, it is difficult to exploit prior knowledge about metal coating defects in end-to-end deep learning and adapt to the variable scale of the defects and the limited training data. This paper proposes a defect segmentation algorithm based on prior knowledge about metal coating defects to unify U-Net, a deep learning segmentation model for automatic metal coating defect recognition. This anomaly segmentation is based on Hue channel distribution and edge response. Being trained in a knowledge driven manner, the model can exclude outliers from training data and effectively avoid over-fitting. On a metal coating defect image dataset with four defect types, including crack, blister, rusting and flaking, the proposed method achieves 81.24% mIoU, which is advantageous over end-to-end deep learning. The experiment shows that knowledge-driven model can boost the performance of deep learning models in metal coating defect segmentation.

     

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