基于人工智能的裂纹检测的数据驱动方法:技术、挑战和未来范围

IF 2.4 Q3 ENVIRONMENTAL SCIENCES
Priti S. Chakurkar, Deepali Vora, Shruti Patil, Sashikala Mishra, Ketan Kotecha
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引用次数: 0

摘要

本文对人工智能(AI)技术在民用基础设施裂缝检测中的应用进行了系统的文献综述,这是影响这些结构性能和寿命的关键问题。传统的裂纹检测方法涉及人工检测,费时费力,特别是在城市地区。因此,人工智能自动裂缝检测技术由于能够实时识别道路退化,从而提高了安全性和可靠性而受到欢迎。这篇综述强调了裂缝检测的两种关键方法:深度学习和传统计算机视觉,重点关注数据驱动方面,主要依赖于来自训练数据集的数据来检测和量化裂缝的严重程度。本文重点介绍了每种方法的优缺点,并概述了各种裂纹检测模型、特征提取技术、数据集、潜在问题和未来方向。研究表明,基于深度学习的裂缝分类、定位和分割方法比传统的计算机视觉技术表现出更好的性能,特别是在精度方面。然而,深度学习方法需要大量的训练数据和计算能力,这可能是一个很大的限制。此外,本文还指出了3D数据集的缺乏,无监督学习算法很少用于训练裂缝检测模型,以及具有各种道路纹理(如沥青和水泥等)的道路图像的数据集是该领域未来研究的挑战。对三维和组合纹理数据集的需求是该领域未来研究的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven approach for AI-based crack detection: techniques, challenges, and future scope
This article provides a systematic literature review on the application of artificial intelligence (AI) technology for detecting cracks in civil infrastructure, which is a critical issue affecting the performance and longevity of these structures. Traditional crack detection methods involve manual inspection, which is laborious and time-consuming, especially in urban areas. Therefore, automatic crack detection with AI technology has gained popularity due to its ability to identify degradation of roads in real-time, leading to increased safety and reliability. This review emphasizes two key approaches for crack detection: deep learning and traditional computer vision, with a focus on data-driven aspects that rely primarily on data from training datasets to detect and quantify the severity level of the crack. The article highlights the advantages and drawbacks of each approach and provides an overview of various crack detection models, feature extraction techniques, datasets, potential issues, and future directions. The research concludes that deep learning-based methods used for crack classification, localization and segmentation have shown better performance than traditional computer vision techniques, especially in terms of accuracy. However, deep learning methods require large amounts of training data and computational power, which can be a significant limitation. Additionally, the article identifies a lack of 3D datasets, unsupervised learning algorithms are rarely used to train crack detection model, and datasets having road images with variety of road textures such as asphalt and cement etc. as challenges for future research in this field. A need for 3D and combined texture datasets as challenges for future research in this field.
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来源期刊
CiteScore
4.00
自引率
7.10%
发文量
176
审稿时长
13 weeks
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