基于蒸馏的织物异常检测
Distillation-based fabric anomaly detection
AI总结：
本文提出一种基于反向知识蒸馏的织物异常检测方法，通过编码器-解码器设计突出高层特征，在多个纹理数据集上验证了性能与推理速度，并贡献了新数据集。
摘要：
无监督纹理异常检测在大量工业过程中一直是一个备受关注的话题。图案纹理检测，特别是在织物缺陷检测的背景下，确实是一个广泛遇到的应用场景。该任务涉及处理多种颜色和纺织品类型，涵盖广泛的织物。鉴于颜色、纹理和缺陷类型的广泛变异性，织物缺陷检测在图案纹理检测领域提出了一个复杂且具有挑战性的问题。在本文中，我们提出了一种基于知识蒸馏的方法，专门用于解决类似织物的纹理中的无监督异常检测挑战。我们的方法旨在重新定义最近引入的反向蒸馏方法，该方法主张采用编码器-解码器设计以减轻分类器偏差并防止学生网络重建异常。在本研究中，我们针对织物缺陷检测这一特定任务提出了一种新的反向蒸馏技术。我们的方法涉及精心选择设计，战略性地突出高层特征。为了展示我们的方法在性能和推理速度方面的能力，我们在多个纹理数据集上进行了系列实验，包括MVTEC AD、AITEX和TILDA，同时还在从纺织制造设施获取的数据集上进行了实验。本文的主要贡献如下：一种利用反向知识蒸馏技术的鲁棒纹理异常检测器，适用于异常检测和领域泛化；以及一个包含多种织物和缺陷的新颖数据集。
英文摘要：
Unsupervised texture anomaly detection has been a concerning topic in a vast amount of industrial processes. Patterned textures inspection, particularly in the context of fabric defect detection, is indeed a widely encountered use case. This task involves handling a diverse spectrum of colors and textile types, encompassing a wide range of fabrics. Given the extensive variability in colors, textures, and defect types, fabric defect detection poses a complex and challenging problem in the field of patterned textures inspection. In this article, we propose a knowledge distillation-based approach tailored specifically for addressing the challenge of unsupervised anomaly detection in textures resembling fabrics. Our method aims to redefine the recently introduced reverse distillation approach, which advocates for an encoder-decoder design to mitigate classifier bias and to prevent the student from reconstructing anomalies. In this study, we present a new reverse distillation technique for the specific task of fabric defect detection. Our approach involves a meticulous design selection that strategically highlights high-level features. To demonstrate the capabilities of our approach both in terms of performance and inference speed, we conducted a series of experiments on multiple texture datasets, including MVTEC AD, AITEX, and TILDA, alongside conducting experiments on a dataset acquired from a textile manufacturing facility. The main contributions of this paper are the following: a robust texture anomaly detector utilizing a reverse knowledge-distillation technique suitable for both anomaly detection and domain generalization and a novel dataset encompassing a diverse range of fabrics and defects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
