Regular inspections of bridge substructures are very important for evaluating bridge health, since early detection and assessment offer the best chances of bridge repair. However, the traditional inspection methods of checking defects with visual features cannot meet engineering needs sufficiently. Although deep-learning methods have recently demonstrated a remarkable improvement in image classification and recognition, there are still difficulties, such as the countless parameters and large model training sets needed by these methods. In this paper, we propose a novel crack extraction algorithm for automatic segmentation of cracks and noise using multi-layer features extracted from a fully convolutional network and a naive Bayes data fusion (NB-FCN) model. The bridge images in both the training and testing datasets are taken using an in-house designed high-precision image acquisition device, called Bridge Substructure Detection 10 (BSD-10). BSD-10 is applied to collect 7200 ima...

中文翻译：

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基于全卷积神经网络和基于视觉检测系统的朴素贝叶斯数据融合的混凝土桥梁自动裂缝识别

定期检查桥梁下部结构对于评估桥梁的健康状况非常重要，因为早期检测和评估可以提供桥梁修复的最佳机会。但是，传统的检查具有视觉特征的缺陷的检查方法不能充分满足工程需求。尽管深度学习方法最近在图像分类和识别方面显示出显着的进步，但仍然存在困难，例如这些方法需要无数的参数和大型模型训练集。在本文中，我们提出了一种新颖的裂缝提取算法，该算法利用从全卷积网络和朴素贝叶斯数据融合（NB-FCN）模型提取的多层特征，对裂缝和噪声进行自动分割。训练和测试数据集中的桥梁图像都是使用内部设计的高精度图像采集设备采集的，该设备称为桥梁子结构检测10（BSD-10）。BSD-10用于收集7200个图像