Road crack detection and quantification based on segmentation network using architecture of matrix

PurposePeriodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.Design/methodology/approachIn this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.FindingsTo improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.Originality/valueThis paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

[1]  V. F. Leavers,et al.  Which Hough transform , 1993 .

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

[3]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Fan Xi,et al.  Detection crack in image using Otsu method and multiple filtering in image processing techniques , 2016 .

[5]  Nikolaos Doulamis,et al.  Deep Convolutional Neural Networks for efficient vision based tunnel inspection , 2015, 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).

[6]  Paulo Lobato Correia,et al.  Automatic Road Crack Detection and Characterization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[7]  Naim Dahnoun,et al.  A Novel Disparity Transformation Algorithm for Road Segmentation , 2018, Inf. Process. Lett..

[8]  Chiou-Shann Fuh,et al.  Hierarchical color image region segmentation for content-based image retrieval system , 2000, IEEE Trans. Image Process..

[9]  Guanghui Wang,et al.  Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection , 2020, IEEE Access.

[10]  Qian Wang,et al.  DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection , 2019, IEEE Transactions on Image Processing.

[11]  Dacheng Tao,et al.  Anchor Cascade for Efficient Face Detection , 2018, IEEE Transactions on Image Processing.

[12]  Peggy Subirats,et al.  Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform , 2006, 2006 International Conference on Image Processing.

[13]  Hae-Bum Yun,et al.  Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack Detection , 2016 .

[14]  Yong Hu,et al.  A novel LBP based methods for pavement crack detection , 2009 .

[15]  Rih-Teng Wu,et al.  A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces , 2017, Comput. Aided Civ. Infrastructure Eng..

[16]  Hai Le Vu,et al.  An effective spatial-temporal attention based neural network for traffic flow prediction , 2019, Transportation Research Part C: Emerging Technologies.

[17]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[20]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[21]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

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

[23]  Qingquan Li,et al.  CrackTree: Automatic crack detection from pavement images , 2012, Pattern Recognit. Lett..

[24]  Chaobo Zhang,et al.  Concrete bridge surface damage detection using a single‐stage detector , 2019, Comput. Aided Civ. Infrastructure Eng..

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

[26]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Sami F. Masri,et al.  Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures , 2012 .

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

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

[30]  Mu-Chun Su,et al.  A self organizing map optimization based image recognition and processing model for bridge crack inspection , 2017 .

[31]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[33]  Qingquan Li,et al.  Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method , 2008, 2008 Congress on Image and Signal Processing.

[34]  Hyoungkwan Kim,et al.  Encoder–decoder network for pixel‐level road crack detection in black‐box images , 2019, Comput. Aided Civ. Infrastructure Eng..

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

[36]  Sidney Nascimento Givigi,et al.  Automatic Crack Detection and Measurement Based on Image Analysis , 2016, IEEE Transactions on Instrumentation and Measurement.

[37]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, ECCV.

[39]  José Manuel Menéndez,et al.  A Simplified Computer Vision System for Road Surface Inspection and Maintenance , 2016, IEEE Transactions on Intelligent Transportation Systems.

[40]  Anders Landström,et al.  Morphology-Based Crack Detection for Steel Slabs , 2012, IEEE Journal of Selected Topics in Signal Processing.

[41]  Wei Li,et al.  CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection , 2020, Structural Control and Health Monitoring.

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

[43]  H. R. Keshavan,et al.  An optimal multiple threshold scheme for image segmentation , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[44]  Hyun Jong Lee,et al.  A two-step sequential automated crack detection and severity classification process for asphalt pavements , 2020, International Journal of Pavement Engineering.

[45]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[46]  Horst-Michael Groß,et al.  How to get pavement distress detection ready for deep learning? A systematic approach , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[47]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[48]  Fan Chen,et al.  A semi-fragile image watermarking algorithm with two-stage detection , 2012, Multimedia Tools and Applications.

[49]  Naim Dahnoun,et al.  Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation , 2017, IEEE Transactions on Intelligent Transportation Systems.

[50]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.