Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
复制标题
使用深度学习网络对 3D 沥青表面进行自动像素级路面裂缝检测
DOI：
10.1111/mice.12297
复制
发表时间：
2017-10-01
影响因子：
9.6
通讯作者：
Chen, Cheng
中科院分区：
文献类型：
--
作者：
Zhang, Allen;Wang, Kelvin C. P.;Chen, Cheng
CrackNet是一种基于卷积神经网络（CNN）的高效架构，本文提出了用于3D沥青表面上的自动路面裂缝检测，其明确目标是像素完美的精度。与常用的CNN不同，CrackNet没有任何池化层，可以缩小先前层的输出。CrackNet从根本上确保像素完美的准确性，使用新开发的技术不变的图像宽度和高度通过所有层。CrackNet由五层组成，包括在学习过程中训练的一百多万个参数。CrackNet的输入数据是由特征提取器使用所提出的具有各种方向、宽度和长度的线过滤器生成的特征图。CrackNet的输出是所有像素的预测类分数集。CrackNet的隐藏层是卷积层和全连接层。CrackNet使用1，800张3D路面图像进行训练，然后使用另一组200张3D路面图像在各种条件下成功检测裂缝。使用200个测试3D图像的实验表明，CrackNet可以同时实现高精度（90.13%），召回率（87.63%）和F-测量（88.86%）。与最近开发的基于传统机器学习和成像算法的裂缝检测方法相比，CrackNet在F度量方面明显优于传统方法。使用并行计算技术，CrackNet被编程为与数据收集软件结合使用。
The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.