In industrial manufacturing processes, errors frequently occur at
unpredictable times and in unknown manifestations. We tackle the problem of
automatic defect detection without requiring any image samples of defective
parts. Recent works model the distribution of defect-free image data, using
either strong statistical priors or overly simplified data representations. In
contrast, our approach handles fine-grained representations incorporating the
global and local image context while flexibly estimating the density. To this
end, we propose a novel fully convolutional cross-scale normalizing flow
(CS-Flow) that jointly processes multiple feature maps of different scales.
Using normalizing flows to assign meaningful likelihoods to input samples
allows for efficient defect detection on image-level. Moreover, due to the
preserved spatial arrangement the latent space of the normalizing flow is
interpretable which enables to localize defective regions in the image. Our
work sets a new state-of-the-art in image-level defect detection on the
benchmark datasets Magnetic Tile Defects and MVTec AD showing a 100% AUROC on 4
out of 15 classes.

本研究提出了一种新的全卷积跨尺度归一化流模型（CS-Flow），该模型通过对不同尺度的多个特征图进行联合处理，能够对工业制造中的缺陷进行自动检测和定位，同时在基准数据集上取得了 100％的区分度（AUROC)。

基于图像的缺陷检测的全卷积跨尺度流

Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection

Electroluminescence (EL) imaging is a useful modality for the inspection of
photovoltaic (PV) modules. EL images provide high spatial resolution, which
makes it possible to detect even finest defects on the surface of PV modules.
However, the analysis of EL images is typically a manual process that is
expensive, time-consuming, and requires expert knowledge of many different
types of defects. In this work, we investigate two approaches for automatic
detection of such defects in a single image of a PV cell. The approaches differ
in their hardware requirements, which are dictated by their respective
application scenarios. The more hardware-efficient approach is based on
hand-crafted features that are classified in a Support Vector Machine (SVM). To
obtain a strong performance, we investigate and compare various processing
variants. The more hardware-demanding approach uses an end-to-end deep
Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit
(GPU). Both approaches are trained on 1,968 cells extracted from high
resolution EL intensity images of mono- and polycrystalline PV modules. The CNN
is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a
slightly lower average accuracy of 82.44%, but can run on arbitrary hardware.
Both automated approaches make continuous, highly accurate monitoring of PV
cells feasible.

研究使用基于手工特征和基于深度卷积神经网络（CNN）的两种方法，在高分辨率电致发光（EL）图像中自动检测光伏电池的缺陷，其中 CNN 方法准确率达到 88.42％。