Pavement crack detection and recognition using the architecture of segNet

Abstract This paper presents a practical deep-learning-based crack detection model for inspecting concrete pavement, asphalt pavement, and bridge deck cracks. Crack detection is a typical semantic segmentation task; thus, we propose an encoder-decoder structural model with a fully convolutional neural network, namely, PCSN, by referring to SegNet. This model accepts images of arbitrary size as input data and can be trained pixel by pixel. Moreover, VGG16 net is adopted without the top layer as the encoder, and it is initialized with open-source pretrained weights. “Adadelta” is employed as the optimizer and the cross-entropy is used as the loss function. a crack dataset of images containing complex crack textures is constructed by manual pixelwise annotation. Finally, the dataset is fed into PCSN to train and test the network. FCN-8s and MRCNN are also trained with the same dataset, and the experimental results demonstrate that the PCSN outperforms other algorithm on crack detection, additionally, the basic principle of methodological integration is also briefly introduced.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yong Chen,et al.  A Survey on Industrial Information Integration 2016–2019 , 2020 .

[4]  Yong Chen,et al.  Industrial information integration - A literature review 2006-2015 , 2016, J. Ind. Inf. Integr..

[5]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[6]  ChaYoung-Jin,et al.  Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks , 2017 .

[7]  Fereidoon Moghadas Nejad,et al.  Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review , 2017 .

[8]  Giorgio Valentini,et al.  Ensembles of Learning Machines , 2002, WIRN.

[9]  李良福 Li Liangfu,et al.  Bridge Crack Detection Algorithm Based on Image Processing under Complex Background , 2019 .

[10]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alexey Finogeev,et al.  Intelligent monitoring system for smart road environment , 2019, J. Ind. Inf. Integr..

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Rigoberto Burgueño,et al.  Structural information integration for predicting damages in bridges , 2019, J. Ind. Inf. Integr..

[14]  Shih-Ching Yeh,et al.  Classification of multichannel surface-electromyography signals based on convolutional neural networks , 2019, J. Ind. Inf. Integr..

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Y. Ho,et al.  Simple Explanation of the No-Free-Lunch Theorem and Its Implications , 2002 .

[17]  Kincho H. Law,et al.  Automatic localization of casting defects with convolutional neural networks , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[18]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[19]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[20]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[21]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[22]  Rasim M. Alguliyev,et al.  Privacy-preserving deep learning algorithm for big personal data analysis , 2019, J. Ind. Inf. Integr..

[23]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  David H. Wolpert,et al.  Remarks on a recent paper on the "no free lunch" theorems , 2001, IEEE Trans. Evol. Comput..

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[27]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[28]  Donghan Lee,et al.  Robust Concrete Crack Detection Using Deep Learning-Based Semantic Segmentation , 2019, International Journal of Aeronautical and Space Sciences.

[29]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[30]  Yang Lu,et al.  Industrial Integration: A Literature Review , 2016 .

[31]  Bart De Schutter,et al.  Deep convolutional neural networks for detection of rail surface defects , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).