可用性(结构)
卷积神经网络
耐久性
计算机科学
人工智能
图像处理
人工神经网络
可靠性(半导体)
鉴定(生物学)
曲面(拓扑)
噪音(视频)
目视检查
数字图像
结构工程
计算机视觉
模式识别(心理学)
图像(数学)
工程类
数据库
数学
几何学
量子力学
生物
植物
物理
功率(物理)
作者
Hyun-Jun Kim,Eunjong Ahn,Myoungsu Shin,Sung‐Han Sim
标识
DOI:10.1177/1475921718768747
摘要
In concrete structures, surface cracks are important indicators of structural durability and serviceability. Generally, concrete cracks are visually monitored by inspectors who record crack information such as the existence, location, and width. Manual visual inspection is often considered ineffective in terms of cost, safety, assessment accuracy, and reliability. Digital image processing has been introduced to more accurately obtain crack information from images. A critical challenge is to automatically identify cracks from an image containing actual cracks and crack-like noise patterns (e.g. dark shadows, stains, lumps, and holes), which are often seen in concrete structures. This article presents a methodology for identifying concrete cracks using machine learning. The method helps in determining the existence and location of cracks from surface images. The proposed approach is particularly designed for classifying cracks and noncrack noise patterns that are otherwise difficult to distinguish using existing image processing algorithms. In the training stage of the proposed approach, image binarization is used to extract crack candidate regions; subsequently, classification models are constructed based on speeded-up robust features and convolutional neural network. The obtained crack identification methods are quantitatively and qualitatively compared using new concrete surface images containing cracks and noncracks.
科研通智能强力驱动
Strongly Powered by AbleSci AI