一种面向工业产品表面缺陷图像的色调增强方法

Hue Augmentation Method for Industrial Product Surface Defect Images

在基于深度学习的工业缺陷检测中,采样数据的色调分布、缺陷的位置分布往往与检测数据存在着差异,这会导致检测模型性能不佳,基于GAN(Generative Adversarial Networks)的数据增强方法为常用的解决方法,文中设计了HC-GAN和T-GAN来分别进行色调和缺陷位置的增强。在HC-GAN中,通过构建语义保持模块和色调控制模块,能够在不改变缺陷特征的前提下实现基于参考数据的色调增强;在T-GAN中,通过输入、输出数据的成对设定,实现了缺陷位置转移;在实际应用中,两个GAN的串联使用能降低训练数据在色调和空间上的不均衡性,提高了模型的检测性能。最后进行了实验验证,结果表明,所提方法生成的数据实现了缺陷图像的色调增强和位置增强,提高了工业产品表面缺陷检测的精度。

The hue distribution of industrial sampling data and the spatial distribution of defects are often different from test data,which often leads to poor performance of defect detection models based on deep learning.Therefore,data augmentation based on generative adversarial networks(GAN)is a common solution.Two GANs(HC-GAN and T-GAN)are designed to perform hue augmentation and defect location augmentation respectively.By constructing content consistency module and hue controlled module,HC-GAN can achieve hue augmentation based on reference data without changing defect characteristics.By pairing the input and output data,T-GAN realizes the defect location transfer.In addition,two GANs can also be used in tandem to achieve both hue augmentation and position transfer.Finally,hue distribution statistics and object detection effect tests are carried out on the generated data.The results show that the data generated by the proposed method can achieve hue augmentation and position augmentation,and improve the accuracy of surface defect detection of industrial products.