论文标题
使用双通道深卷积网络检测混凝土裂纹
Detection Of Concrete Cracks using Dual-channel Deep Convolutional Network
论文作者
论文摘要
由于循环负荷和疲劳应力裂纹，这会影响任何民用基础设施的安全。如今，机器视觉已被用来帮助我们通过部分置换人类导入的现场检查来适当维护，监视和检查混凝土结构。当前的研究提出了一种基于深卷积神经网络（CNN）的裂纹检测方法，用于检测混凝土裂纹，而无需明确计算缺陷特征。在研究过程中，已经创建了一个具有混凝土裂纹的3200个标记图像的数据库，其中对比度，照明条件，方向和裂纹的严重程度极具可变。在本文中，从经过256 x 256像素分辨率的这些图像训练的深CNN开始，我们通过识别困难逐渐优化了该模型。使用增强数据集（考虑到与无人机视频兼容的变化和降解），例如随机缩放，旋转，旋转和强度缩放和详尽的消融研究，我们设计了一个双通道深的CNN，它显示出很高的精度（〜92.25％），并在寻找更加裂纹的认真裂缝中，以寻找更高的裂缝。该模型已根据性能进行了测试，并在特征图的帮助下进行了分析，该图形确定了双通道结构的重要性。
Due to cyclic loading and fatigue stress cracks are generated, which affect the safety of any civil infrastructure. Nowadays machine vision is being used to assist us for appropriate maintenance, monitoring and inspection of concrete structures by partial replacement of human-conducted onsite inspections. The current study proposes a crack detection method based on deep convolutional neural network (CNN) for detection of concrete cracks without explicitly calculating the defect features. In the course of the study, a database of 3200 labelled images with concrete cracks has been created, where the contrast, lighting conditions, orientations and severity of the cracks were extremely variable. In this paper, starting from a deep CNN trained with these images of 256 x 256 pixel-resolution, we have gradually optimized the model by identifying the difficulties. Using an augmented dataset, which takes into account the variations and degradations compatible to drone videos, like, random zooming, rotation and intensity scaling and exhaustive ablation studies, we have designed a dual-channel deep CNN which shows high accuracy (~ 92.25%) as well as robustness in finding concrete cracks in realis-tic situations. The model has been tested on the basis of performance and analyzed with the help of feature maps, which establishes the importance of the dual-channel structure.
