Detecting surface anomalies of industrial materials poses a significant
challenge within a myriad of industrial manufacturing processes. In recent
times, various methodologies have emerged, capitalizing on the advantages of
employing a network pre-trained on natural images for the extraction of
representative features. Subsequently, these features are subjected to
processing through a diverse range of techniques including memory banks,
normalizing flow, and knowledge distillation, which have exhibited exceptional
accuracy. This paper revisits approaches based on pre-trained features by
introducing a novel method centered on target-specific embedding. To capture
the most representative features of the texture under consideration, we employ
a variant of a contrastive training procedure that incorporates both
artificially generated defective samples and anomaly-free samples during
training. Exploiting the intrinsic properties of surfaces, we derived a
meaningful representation from the defect-free samples during training,
facilitating a straightforward yet effective calculation of anomaly scores. The
experiments conducted on the MVTEC AD and TILDA datasets demonstrate the
competitiveness of our approach compared to state-of-the-art methods.

本研究提出了一种以目标特定嵌入为核心的新方法，通过引入人工生成的有缺陷样本和无缺陷样本，使用对比训练过程捕捉待考虑纹理的最具代表性特征，并利用表面的内在性质，从无缺陷样本中推导出有意义的表示，从而实现了对异常得分的简单而有效的计算。通过在 MVTEC AD 和 TILDA 数据集上进行的实验，验证了我们的方法与最先进方法的竞争力。