近年来, 基于深度学习的表面缺陷检测技术广泛应用在各种工业场景中. 本文对近年来基于深度
学习的表面缺陷检测方法进行了梳理, 根据数据标签的不同将其分为全监督学习模型方法 …

Convolutional autoencoders have emerged as popular methods for unsupervised defect
segmentation on image data. Most commonly, this task is performed by thresholding a pixel …

图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下
, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测 …

It is difficult to detect the surface defects of a lithium battery with an aluminum/steel shell. The
reflectivity, lack of 3D information on the battery surface, and the shortage of many datasets …

In industrial production, flaws and defects inevitably appear on surfaces, resulting in
unqualified products. Therefore, surface defect detection plays a key role in ensuring …

Traditional detection algorithms of pipeline non-destructive testing extract information from a
large number of defect samples to ensure the detection performance, but even if an …

With the development of railway, high speed and intensive rolling will significantly increase
the probability of fatigue damage and they lay a hidden danger for major safety accidents …

Anomaly detection is a challenging task, especially detecting and segmenting tiny defect
regions in images without anomaly priors. Although deep encoder‐decoder‐based …

This paper presents a multi-scale mask cross-layer fusion network (MMCF) for industrial
surface defect detection. MMCF exploits the transformer's ability to preserve global …

In the previous era, humans played important roles in all aspects of industrial work.
However, they indisputably made many errors that can be mitigated by automated …