由于裂缝检测对道路维护和交通安全的实际重要性，路面裂缝检测长期以来一直是一个热门的研究课题。已经提出了许多方法来解决这个问题。本文回顾了道路裂缝检测中使用的三种主要方法：图像处理、机器学习和基于 3D 成像的方法。图像处理算法主要包括阈值分割、边缘检测和区域生长方法，用于处理图像和识别裂纹特征。基于神经网络和支持向量机等传统机器学习方法的裂纹检测仍然依赖于使用图像处理技术手工制作的特征。深度学习方法从根本上改变了裂纹检测的方式，大大提高了检测性能。在这项工作中，我们以基于分类、基于对象检测和基于分割的三种方式审查和比较了在裂缝检测中提出的深度学习神经网络。我们还介绍了性能评估指标和这些方法在常用基准数据集上的性能。随着 3D 技术的成熟，利用 3D 数据进行裂纹检测是一个新的研究和应用方向。我们比较了三种类型的 3D 数据表示，并研究了用于 3D 对象检测的深度神经网络的相应性能。还详细回顾了使用 3D 数据的传统和基于深度学习的裂纹检测方法。我们还介绍了性能评估指标和这些方法在常用基准数据集上的性能。随着 3D 技术的成熟，利用 3D 数据进行裂纹检测是一个新的研究和应用方向。我们比较了三种类型的 3D 数据表示，并研究了用于 3D 对象检测的深度神经网络的相应性能。还详细回顾了使用 3D 数据的传统和基于深度学习的裂纹检测方法。我们还介绍了性能评估指标和这些方法在常用基准数据集上的性能。随着 3D 技术的成熟，利用 3D 数据进行裂纹检测是一个新的研究和应用方向。我们比较了三种类型的 3D 数据表示，并研究了用于 3D 对象检测的深度神经网络的相应性能。还详细回顾了使用 3D 数据的传统和基于深度学习的裂纹检测方法。
Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail.
