Civil infrastructure (eg, buildings, roads, underground tunnels) could lose its expected
physical and functional conditions after years of operation. Timely and accurate inspection …

Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …

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 …

State-of-the-art machine-vision systems have limitations associated with crack width
measurements. The sample points used to describe the crack width are often subjectively …

Masonry structures represent the highest proportion of building stock worldwide. Currently,
the structural condition of such structures is predominantly manually inspected which is a …

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 …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

Cracking is a common pavement distress that would cause further severe problems if not
repaired timely, which means that it is important to accurately extract the information of …

Previous research has shown the high accuracy of convolutional neural networks (CNNs) in
asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation …

With the advancement of machine learning (ML) and deep learning (DL), there is a great
opportunity to enhance the development of automatic crack detection algorithms. In this …