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 …

在半导体, PCB, 汽车装配, 液晶屏, 3C, 光伏电池, 纺织等行业中, 产品外观与产品性能有着千丝
万缕的联系. 表面缺陷检测是阻止残次品流入市场的重要手段. 利用机器视觉的技术进行检测 …

Detecting surface defects plays a crucial role in ensuring the quality, functionality, and
security of the production process. Traditional image processing techniques and machine …

In recent years, deep learning methods have been widely used in various industrial
scenarios, promoting industrial intelligence. Real-time surface defect inspection of industrial …

In recent years, with the rapid development of deep learning, computer vision technology
based on convolutional neural network (CNN) is widely used in industrial fields. At present …

Automated surface-anomaly detection using machine learning has become an interesting
and promising area of research, with a very high and direct impact on the application …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

Surface defect detection is a critical task in product quality assurance for manufacturing
lines. The deep learning-based methods recently developed for defect detection are …

Deep-learning methods have recently started being employed for addressing surface-defect
detection problems in industrial quality control. However, with a large amount of data …

This article presents a deep learning scheme for automatic defect detection in material
surfaces. The success of deep learning model training is generally determined by the …