The current bridge maintenance practice generally involves manual visual inspection, which is highly subjective and unreliable. A technique that can automatically detect defects, for example, surface cracks, is essential so that early warnings can be triggered to prevent disaster due to structural failure. In this study, to permit automatic identification of concrete cracks, an ad‐hoc faster region‐based convolutional neural network (faster R‐CNN) was applied to contaminated real‐world images taken from concrete bridges with complex backgrounds, including handwriting. A dataset of 5,009 cropped images was generated and labeled for two different objects, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Four full‐scale images that contained complex disturbance information were used to assess the performance of the trained network. The results of this study demonstrate that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts. For comparative study, the proposed network is also compared with You Only Look Once v2 detection technique.

中文翻译：

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使用更快的基于区域的卷积神经网络检测带有手写脚本干扰的混凝土裂缝

当前的桥梁维护实践通常涉及手动外观检查，这是高度主观且不可靠的。能够自动检测缺陷（例如表面裂缝）的技术是必不可少的，因此可以触发预警来防止由于结构故障而造成的灾难。在这项研究中，为了允许自动识别混凝土裂缝，将基于区域的临时快速卷积神经网络（更快的R-CNN）应用于从具有复杂背景（包括手写）的混凝土桥梁拍摄的污染的真实世界图像中。生成了5009张裁剪图像的数据集，并标记了两个不同的对象（裂缝和笔迹）。然后使用生成的图像数据集对建议的网络进行培训和测试。包含复杂干扰信息的四张全尺寸图像用于评估训练网络的性能。这项研究的结果表明，即使存在手写脚本，更快的R-CNN仍可以自动定位原始图像中的裂缝。为了进行比较研究，还将提议的网络与You Only Look Once v2检测技术进行了比较。