Deep Learning-Based Crack Detection Using Mask R-CNN Technique
Deep Learning-Based Crack Detection Using Mask R-CNN Technique
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发表时间：
2019
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通讯作者：
Chengjun Tan;N. Uddin;Y. M. Mohammed
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作者：
Chengjun Tan;N. Uddin;Y. M. Mohammed
桥梁、水坝、道路和摩天大楼等民用基础设施的裂缝可能会降低局部刚度，造成材料不连续，从而失去其设计功能，威胁公共安全。这个不可避免的过程意味着紧急的维护问题。早期发现可以采取预防措施，防止损坏和可能的故障。随着图像数据量的不断增加，基于机器/深度学习的方法已经成为图像裂缝检测的一个重要分支。这项研究是利用最先进的技术，即掩模区域卷积神经网络（R-CNN），建立一个自动裂纹检测器，这是一种深度学习。Mask R-CNN技术是近年来提出的一种用于自然图像中目标检测和目标定位以及目标实例分割的算法。实验结果表明，所构建的裂纹检测器能够对大范围的裂纹图像进行高效的自动分割。此外，这个提议的自动探测器也可以在视频上工作；表明基于掩模R-CNN的探测器在现场实时检测裂缝存在及其形态方面具有鲁棒性和可行性。
Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site.