论文标题
使用卷积神经网络和人工神经网络的表面损伤检测方案
Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network
论文作者
论文摘要
混凝土上的表面损害很重要，因为损坏会影响结构的结构完整性。本文提出了使用卷积神经网络（CNN）和人工神经网络（ANN）的两步表面损伤检测方案。 CNN将给定的图像分为两类：正和负面。正类别是图像中存在表面损伤的位置，否则图像被归类为负。这是基于图像的分类。 ANN接受ANN归类为正的图像输入。这减少了ANN进一步处理的图像数量。 ANN执行基于特征的分类，其中特征是从图像中检测到的边缘提取的。使用Canny Edge检测检测边缘。总共从检测到的边缘提取了19个功能。这些功能是ANN的输入。 ANN的目的是仅突出图像中的正损坏边缘。对于图像分类，CNN的精度为80.7％，而ANN的表面检测精度为98.1％。 CNN的准确性降低是由于假阳性检测引起的，但是误报是耐受性的，而假否定性则没有。两步方案中CNN和ANN的假阴性检测为0％。
Surface damage on concrete is important as the damage can affect the structural integrity of the structure. This paper proposes a two-step surface damage detection scheme using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The CNN classifies given input images into two categories: positive and negative. The positive category is where the surface damage is present within the image, otherwise the image is classified as negative. This is an image-based classification. The ANN accepts image inputs that have been classified as positive by the ANN. This reduces the number of images that are further processed by the ANN. The ANN performs feature-based classification, in which the features are extracted from the detected edges within the image. The edges are detected using Canny edge detection. A total of 19 features are extracted from the detected edges. These features are inputs into the ANN. The purpose of the ANN is to highlight only the positive damaged edges within the image. The CNN achieves an accuracy of 80.7% for image classification and the ANN achieves an accuracy of 98.1% for surface detection. The decreased accuracy in the CNN is due to the false positive detection, however false positives are tolerated whereas false negatives are not. The false negative detection for both CNN and ANN in the two-step scheme are 0%.
