Abstract In industrial production, it is difficult to obtain a well-trained surface detection algorithm since the real defect samples are lacking. In this paper, we propose a surface defect segmentation method based on defect sample simulation, which only needs few defect training samples. The entire method includes two modules: a local defect simulation algorithm and a residual-restored-based segmentation algorithm. In order to ensure both structural and local texture consistency of the simulated defects, we design a two-stage simulation algorithm based on generation adversarial net and neural style transfer. The simulation method requires one single defect reference sample for training, and can generate the same type of defect in the specified area. The segmentation algorithm, trained with the simulated images and reference samples, can restore the defect area and yield the predicted label from the residual image. We carry out experiments on the button, road crack, and silicon steel strip datasets. The results show that the proposed method can remarkably improve the defect segmentation accuracy, attaining F1 score of 0.82.

中文翻译：

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一种基于仿真的表面缺陷分割小样本学习方法

摘要 在工业生产中，由于缺乏真实的缺陷样本，很难获得训练有素的表面检测算法。在本文中，我们提出了一种基于缺陷样本模拟的表面缺陷分割方法，该方法只需要很少的缺陷训练样本。整个方法包括两个模块：局部缺陷模拟算法和基于残差恢复的分割算法。为了确保模拟缺陷的结构和局部纹理一致性，我们设计了一种基于生成对抗网络和神经风格迁移的两阶段模拟算法。该模拟方法只需要一个单一缺陷参考样本进行训练，可以在指定区域内生成同类型缺陷。用模拟图像和参考样本训练的分割算法，可以恢复缺陷区域并从残差图像中产生预测标签。我们对纽扣、道路裂缝和硅钢带数据集进行了实验。结果表明，所提出的方法可以显着提高缺陷分割的准确性，F1得分为0.82。