论文标题
什么是破裂？对结构裂纹分割，检测和定量的深度学习方法的综述和分析
What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification
论文作者
论文摘要
表面裂纹是潜在结构断层的非常普遍的指标。它们的早期检测和监测是结构性健康监测的重要因素。保持未经治疗，它们可以随着时间的推移而大小，需要昂贵的维修或维护。随着计算机视觉和深度学习算法的最新进展，该监视过程的自动检测和分割已成为一个主要的主题。这篇综述旨在概述研究人员在使用深度学习的裂纹分析算法领域中发表的工作。它概述了通过将计算机视觉算法应用于结构性健康监测环境中的表面裂纹来解决的各种任务，还提供了对最新，半，半和无监督的方法的深入评论，这些方法执行裂纹分类，检测，细分和量化。此外，这篇综述还突出了用于评估这些算法性能的裂纹和用于评估这些算法性能的指标的流行数据集。最后，概述了潜在的研究差距，并提供了进一步的研究方向。
Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.
