基于无监督域适应的跨场景带钢表面缺陷识别

Surface defect identification of cross scene strip based on unsupervised domain adaptation

深度学习模型面对跨场景的带钢表面缺陷识别时存在泛化性能差的问题,为此提出端到端的多级对齐域适应神经网络模型(MADA),实现源域与目标域数据的像素级光照分布对齐与特征级纹理分布对齐.MADA通过无参考像素级光照分布对齐模块和光照校正损失函数,将源域与目标域数据投影到光照子空间,实现源域与目标域的像素级光照分布对齐.利用纹理特征提取器和特征级域鉴别器的对抗学习,实现源域和目标域数据的纹理分布对齐.实验在邯郸钢铁集团带钢表面缺陷数据集的F1指数达到98%,在谢维尔钢铁集团带钢表面缺陷数据集上的F1指数达到86.6%.实验结果表明,与其他域适应方法相比,所提方法具有更好的泛化性能.

In view of the poor generalization performance of the deep learning model at surface defect identification of cross scene strip,an end-to-end multi-level aligned domain adaptation neural network(MADA)was proposed,which could achieve pixel-level illumination distribution alignment and feature-level texture distribution alignment,respectively.The source and target domain data were projected into the illumination subspace by MADA to achieve the pixel-level illumination distribution alignment,through the non-reference pixel-level illumination distribution alignment module and the illumination loss function.The adversarial learning of texture feature extractor and feature-level domain discriminator were used to achieve the texture distribution alignment of the source and target domain.The experiment achieved an F1 measure of 98%in Handan strip surface defect dataset and 86.6%in Severstal strip surface defect dataset.Experimental results showed that the proposed method has better generalization performance than other domain adaptation methods.