Jamali, Amir (2023) Pavement Defect Classification and Localization Using Hybrid Weakly Supervised and Supervised Deep Learning and GIS. Masters thesis, Concordia University.
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Abstract
Automated detection of road defects has historically been challenging for the pavement management industry. As a result, new methods have been developed over the past few years to handle this issue. Most of these methods relied on supervised machine learning techniques, such as object detection and segmentation methods, which need a large, annotated image dataset to train their models. However, annotating pavement defects is difficult and time-consuming due to their ununiformed and complex shapes. To address this challenge, a hybrid pavement defect classification and localization framework using weakly supervised and supervised deep learning methods is proposed in this thesis. This framework has two steps: (1) A robust hierarchical two-level classifier that classifies the defects in images, and (2) A method for defect localization combining weakly supervised and supervised techniques. In the localization method, first, defects are primarily localized using a weakly supervised method (i.e. Class Activation Mapping (CAM)). Next, based on the results of the first classifiers, the defects are segmented from the localized patches obtained in the previous step. The feature maps extracted from the CAM method are used to train a segmentation network once (i.e. U-Net or Mask R-CNN) to localize and segment the defects in the images. Thus, the proposed framework combines the advantages of weakly supervised and supervised methods. The supervised modules in the framework are trained once and can be used for any new data without the need to train. In other words, to use our framework on new dataset only the classifiers should be fine-tuned. Furthermore, the proposed framework introduced an innovative method designed to calculate the maximum crack width in pixels within linear segmented defect patches, derived from the localization module of the proposed framework. This method is particularly advantageous as it provides critical information that can be further employed in the calculation of the Pavement Condition Index (PCI).
Additionally, the proposed method benefits from an asset management inspection system based on Geographic Information System (GIS) technology to prepare the dataset used in the training and testing. Thus, this advanced system serves a dual role within our framework. Firstly, it assists in the assembly and preparation of the dataset used in the model training process, providing a geographically organized collection of images and related data. Secondly, it plays a crucial role in the testing phase, offering a spatially accurate platform for evaluating the effectiveness of the model in real-world scenarios.
A dataset from Georgia State in the USA was used in the case study. The proposed framework obtained high precision of 97%, 88%, 92% and 97% for localizing the alligator, block, longitudinal and transverse cracks, respectively. Considering all factors, such as annotation cost, and performance on the test dataset, the proposed localization method outperforms the supervised localization methods, such as instance segmentation and object detection for localizing road pavement defects
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Jamali, Amir |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Information Systems Security |
| Date: | 8 August 2023 |
| Thesis Supervisor(s): | Hammad, Amin |
| ID Code: | 992777 |
| Deposited By: | Amir Jamali |
| Deposited On: | 16 Nov 2023 19:37 |
| Last Modified: | 16 Nov 2023 19:37 |
References:
[1] N. S. P. Peraka and K. P. Biligiri, “Pavement asset management systems and technologies: A review,” Autom. Constr., vol. 119, 103336, 2020.[2] B. Becerik-Gerber, S. F. Masri, and M. R. Jahanshahi, “An inexpensive vision-based approach for the autonomous detection, localization, and quantification of pavement defects,” in Transportation Research Record (TRR), 2015.
[3] K. T. Chang, J. R. Chang, and J. K. Liu, “Detection of pavement distresses using 3D laser scanning technology,” in Computing in civil engineering, 2005, pp. 1–11.
[4] C. Koch, K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth, “A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure,” Adv. Eng. Inform., vol. 29, no. 2, pp. 196–210, 2015.
[5] S. Mathavan, K. Kamal, and M. Rahman, “A review of three-dimensional imaging technologies for pavement distress detection and measurements,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2353–2362, 2015.
[6] “Practice for Roads and Parking Lots Pavement Condition Index Surveys”, doi: 10.1520/d6433-20 in ASTM international.
[7] J. Chaki and N. Dey, A Beginner’s Guide to Image Preprocessing Techniques. Boca Raton: CRC Press, 2018. doi: 10.1201/9780429441134.
[8] A. M. Khan, “Image Segmentation Methods: A Comparative Study,” in International Journal of Soft Computing and Engineering vol. 3, no. 4, 2013.
[9] J. Chaki and N. Dey, A beginner’s guide to image shape feature extraction techniques. CRC Press, 2019.
[10] H. Zakeri, F. M. Nejad, and A. Fahimifar, “Image based techniques for crack detection, classification and quantification in asphalt pavement: a review,” Arch. Comput. Methods Eng., vol. 24, no. 4, pp. 935–977, 2017.
[11] H.-D. Cheng and M. Miyojim, “Automatic pavement distress detection system,” Inf. Sci., vol. 108, no. 1–4, pp. 219–240, 1998.
[12] H. Zhao, G. Qin, and X. Wang, “Improvement of canny algorithm based on pavement edge detection,” in 2010 3rd International Congress on Image and Signal Processing, IEEE, 2010, pp. 964–967.
[13] I. Abdel-Qader, O. Abudayyeh, and M. E. Kelly, “Analysis of Edge-Detection Techniques for Crack Identification in Bridges,” J. Comput. Civ. Eng., vol. 17, no. 4, pp. 255–263, Oct. 2003, doi: 10.1061/(ASCE)0887-3801(2003)17:4(255).
65
[14] H. Lokeshwor, L. K. Das, and S. Goel, “Robust method for automated segmentation of frames with/without distress from road surface video clips,” J. Transp. Eng., vol. 140, no. 1, pp. 31–41, 2014.
[15] S. Chambon and J.-M. Moliard, “Automatic road pavement assessment with image processing: review and comparison,” Int. J. Geophys., vol. 2011, 2011.
[16] J. A. Acosta, J. L. Figueroa, and R. L. Mullen, “Low-Cost Video Image Processing System for Evaluating Pavement Surface Distress,” Transp. Res. Rec., no. 1348, 1992, Accessed: Apr. 05, 2023. [Online]. Available: https://trid.trb.org/view/370750
[17] C. Chen, H. Seo, C. H. Jun, and Y. Zhao, “Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM,” Int. J. Pavement Eng., vol. 23, no. 9, pp. 3274–3283, Jul. 2022, doi: 10.1080/10298436.2021.1888092.
[18] Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic Road Crack Detection Using Random Structured Forests,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 12, pp. 3434–3445, Dec. 2016, doi: 10.1109/TITS.2016.2552248.
[19] N. Safaei, O. Smadi, A. Masoud, and B. Safaei, “An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification,” International Journal of Pavement Research and Technology, vol. 15, no. 1, pp. 159–172, Jun. 2021, doi: 10.1007/s42947-021-00006-4 .
[20] N. Sholevar, A. Golroo, and S. R. Esfahani, “Machine learning techniques for pavement condition evaluation,” Autom. Constr., vol. 136, 104190, Apr. 2022, doi: 10.1016/j.autcon.2022.104190.
[21] M.-T. Cao, K.-T. Chang, N.-M. Nguyen, V.-D. Tran, X.-L. Tran, and N.-D. Hoang, “Image Processing Based Automatic Detection of Asphalt Pavement Rutting Using a Novel Metaheuristic Optimized Machine Learning Approach,” May 2021, doi: 10.21203/rs.3.rs-334982/v1.
[22] Y.-A. Hsieh and Y. J. Tsai, “Machine Learning for Crack Detection: Review and Model Performance Comparison,” J. Comput. Civ. Eng., vol. 34, no. 5, 04020038, Sep. 2020, doi: 10.1061/(ASCE)CP.1943-5487.0000918.
[23] S. E. Park, S.-H. Eem, and H. Jeon, “Concrete crack detection and quantification using deep learning and structured light,” Constr. Build. Mater., vol. 252, 119096, Aug. 2020, doi: 10.1016/j.conbuildmat.2020.119096.
[24] P. del Río-Barral, M. Soilán, S. M. González-Collazo, and P. Arias, “Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds,” Remote Sens., vol. 14, no. 22, Art. no. 22, Jan. 2022, doi: 10.3390/rs14225866.
[25] M. Zhong, L. Sui, Z. Wang, and D. Hu, “Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid,” Sensors, vol. 20, no. 15, Art. no. 15, Jan. 2020, doi: 10.3390/s20154198.
66
[26] Z. Du, J. Yuan, F. Xiao, and C. Hettiarachchi, “Application of image technology on pavement distress detection: A review,” Measurement, vol. 184, 109900, Nov. 2021, doi: 10.1016/j.measurement.2021.109900.
[27] Kamal, K., Mathavan, S., Zafar, T., Moazzam, I., Ali, A., Ahmad, S.U. and Rahman, M., “Performance assessment of Kinect as a sensor for pothole imaging and metrology,” International Journal of Pavement Engineering, vol. 19, no. 7, pp. 565–576, Jun. 2016, doi: 10.1080/10298436.2016.1187730.
[28] T. Cao, Y. Wang, and S. Liu, “Pavement Crack Detection Based on 3D Edge Representation and Data Communication With Digital Twins,” IEEE Trans. Intell. Transp. Syst., pp. 1–10, 2022, doi: 10.1109/TITS.2022.3194013.
[29] Feng, Huifang, Wen Li, Zhipeng Luo, Yiping Chen, Sarah Narges Fatholahi, Ming Cheng, Cheng Wang, José Marcato Junior, and Jonathan Li., “GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11052–11061, Aug. 2022, doi: 10.1109/tits.2021.3099023.
[30] M. Nasrollahi, N. Bolourian, and A. Hammad, “Concrete surface defect detection using deep neural network based on lidar scanning,” in Proceedings of the CSCE Annual Conference, Laval, Greater Montreal, QC, Canada, 2019, pp. 12–15.
[31] H. Feng, L. Ma, Y. Yu, Y. Chen, and J. Li, “SCL-GCN: Stratified Contrastive Learning Graph Convolution Network for pavement crack detection from mobile LiDAR point clouds,” Int. J. Appl. Earth Obs. Geoinformation, vol. 118, 103248, Apr. 2023, doi: 10.1016/j.jag.2023.103248.
[32] H. Zhou, B. Gao, and W. Wu, “Automatic Crack Detection and Quantification for Tunnel Lining Surface from 3D Terrestrial LiDAR Data,” J. Eng. Res., Sep. 2022, doi: 10.36909/jer.18459.
[33] R. Ravi, D. Bullock, and A. Habib, “Pavement Distress and Debris Detection using a Mobile Mapping System with 2D Profiler LiDAR,” Transp. Res. Rec., vol. 2675, no. 9, pp. 428–438, Sep. 2021, doi: 10.1177/03611981211002529.
[34] C. Lin, G. Sun, L. Tan, B. Gong, and D. Wu, “Mobile LiDAR Deployment Optimization: Towards Application for Pavement Marking Stained and Worn Detection,” IEEE Sens. J., vol. 22, no. 4, pp. 3270–3280, Feb. 2022, doi: 10.1109/JSEN.2022.3140312.
[35] Z. Li, C. Cheng, M.-P. Kwan, X. Tong, and S. Tian, “Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification,” ISPRS International Journal of Geo-Information, vol. 8, no. 1, 39, Jan. 2019, doi: 10.3390/ijgi8010039.
[36] T. Wen, H. Lang, S. Ding, J. J. Lu, and Y. Xing, “PCDNet: Seed Operation–Based Deep Learning Model for Pavement Crack Detection on 3D Asphalt Surface,” J. Transp. Eng. Part B Pavements, vol. 148, no. 2, 04022023, Jun. 2022, doi: 10.1061/JPEODX.0000367.
67
[37] Fei, Yue, Kelvin CP Wang, Allen Zhang, Cheng Chen, Joshua Q. Li, Yang Liu, Guangwei Yang, and Baoxian Li. “Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 273–284, Jan. 2020, doi: 10.1109/tits.2019.2891167.
[38] X. Chen, J. Li, S. Huang, H. Cui, P. Liu, and Q. Sun, “An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm,” Sensors, vol. 21, no. 5, Art. no. 5, Jan. 2021, doi: 10.3390/s21051581.
[39] P. Asadi, H. Mehrabi, A. Asadi, and M. Ahmadi, “Deep Convolutional Neural Networks for Pavement Crack Detection using an Inexpensive Global Shutter RGB-D Sensor and ARM-Based Single-Board Computer,” Transp. Res. Rec., vol. 2675, no. 9, pp. 885–897, Sep. 2021, doi: 10.1177/03611981211004974.
[40] R. Ghosh and O. Smadi, “Automated Detection and Classification of Pavement Distresses using 3D Pavement Surface Images and Deep Learning,” Transp. Res. Rec., vol. 2675, no. 9, pp. 1359–1374, Sep. 2021, doi: 10.1177/03611981211007481.
[41] B. Gao, Y. Pan, C. Li, S. Geng, and H. Zhao, “Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 6063–6081, Jul. 2022, doi: 10.1109/TITS.2021.3076844.
[42] N. Kheradmandi and V. Mehranfar, “A critical review and comparative study on image segmentation-based techniques for pavement crack detection,” Constr. Build. Mater., vol. 321, 126162, Feb. 2022, doi: 10.1016/j.conbuildmat.2021.126162.
[43] Y. Zhou, X. Guo, F. Hou, and J. Wu, “Review of Intelligent Road Defects Detection Technology,” Sustainability, vol. 14, no. 10, 6306, May 2022, doi: 10.3390/su14106306.
[44] H. Majidifard, Y. Adu-Gyamfi, and W. G. Buttlar, “Deep machine learning approach to develop a new asphalt pavement condition index,” Constr. Build. Mater., vol. 247, 118513, 2020.
[45] J. Ren, G. Zhao, Y. Ma, D. Zhao, T. Liu, and J. Yan, “Automatic Pavement Crack Detection Fusing Attention Mechanism,” Electronics, vol. 11, no. 21, 3622, Nov. 2022, doi: 10.3390/electronics11213622.
[46] F.-J. Du and S.-J. Jiao, “Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection,” Sensors, vol. 22, no. 9, p. 3537, May 2022, doi: 10.3390/s22093537.
[47] Y. Du, N. Pan, Z. Xu, F. Deng, Y. Shen, and H. Kang, “Pavement distress detection and classification based on YOLO network,” International Journal of Pavement Engineering, vol. 22, no. 13, pp. 1659–1672, Jan. 2020, doi: 10.1080/10298436.2020.1714047.
68
[48] Y. K. Yik, N. E. Alias, Y. Yusof, and S. Isaak, “A Real-time Pothole Detection Based on Deep Learning Approach,” J. Phys. Conf. Ser., vol. 1828, no. 1, 012001, Feb. 2021, doi: 10.1088/1742-6596/1828/1/012001.
[49] Y. Zhang, J. Huang, and F. Cai, “On Bridge Surface Crack Detection Based on an Improved YOLO v3 Algorithm,” IFAC-Pap., vol. 53, no. 2, pp. 8205–8210, Jan. 2020, doi: 10.1016/j.ifacol.2020.12.1994.
[50] J. Liu, X. Yang, and V. C. S. Lee, “Automated pavement crack detection using region-based convolutional neural network,” in Functional Pavements, CRC Press, 2020.
[51] S. A. Hassan, S. H. Han, and S. Y. Shin, “Real-time Road Cracks Detection based on Improved Deep Convolutional Neural Network,” in 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Aug. 2020, pp. 1–4. doi: 10.1109/CCECE47787.2020.9255771.
[52] H. Deng, J. Cheng, T. Liu, B. Cheng, and Z. Sun, “Research on Iron Surface Crack Detection Algorithm Based on Improved YOLOv4 Network,” J. Phys. Conf. Ser., vol. 1631, no. 1, 012081, Sep. 2020, doi: 10.1088/1742-6596/1631/1/012081.
[53] D. Jeong, “Road Damage Detection Using YOLO with Smartphone Images,” in 2020 IEEE International Conference on Big Data (Big Data), Dec. 2020, pp. 5559–5562. doi: 10.1109/BigData50022.2020.9377847.
[54] Z. Deyin, W. Penghui, T. Mingwei, C. Conghan, W. Li, and H. Wenxuan, “Investigation of Aircraft Surface Defects Detection Based on YOLO Neural Network,” in 2020 7th International Conference on Information Science and Control Engineering (ICISCE), Dec. 2020, pp. 781–785. doi: 10.1109/ICISCE50968.2020.00165.
[55] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788. Accessed: Mar. 19, 2023. [Online]. Available: https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html
[56] M. Carranza-García, J. Torres-Mateo, P. Lara-Benítez, and J. García-Gutiérrez, “On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data,” Remote Sensing, vol. 13, no. 1, Dec. 2020, doi: 10.3390/rs13010089.
[57] Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X. and Yang, H., “Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN,” Sensors, vol. 22, no. 3, Feb. 2022, doi: 10.3390/s22031215.
[58] R. Li, J. Yu, F. Li, R. Yang, Y. Wang, and Z. Peng, “Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN,” Constr. Build. Mater., vol. 362, 129659, Jan. 2023, doi: 10.1016/j.conbuildmat.2022.129659.
69
[59] K. Hacıefendioğlu and H. B. Başağa, “Concrete Road Crack Detection Using Deep Learning-Based Faster R-CNN Method,” Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol. 46, no. 2, pp. 1621–1633, Jun. 2021, doi: 10.1007/s40996-021-00671-2.
[60] E. Ibragimov, H.-J. Lee, J.-J. Lee, and N. Kim, “Automated pavement distress detection using region based convolutional neural networks,” Int. J. Pavement Eng., vol. 23, no. 6, pp. 1981–1992, May 2022, doi: 10.1080/10298436.2020.1833204.
[61] C. Gou, B. Peng, T. Li, and Z. Gao, “Pavement Crack Detection Based on the Improved Faster-RCNN,” in 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nov. 2019, pp. 962–967. doi: 10.1109/ISKE47853.2019.9170456.
[62] H. Alzraiee, A. Leal Ruiz, and R. Sprotte, “Detecting of Pavement Marking Defects Using Faster R-CNN,” J. Perform. Constr. Facil., vol. 35, no. 4, 04021035, Aug. 2021, doi: 10.1061/(ASCE)CF.1943-5509.0001606.
[63] J. Zhai, Z. Sun, J. Huyan, W. Li, and H. Yang, “Feature representation improved Faster R-CNN model for high-efficiency pavement crack detection,” Can. J. Civ. Eng., vol. 50, no. 2, pp. 114–125, Feb. 2023, doi: 10.1139/cjce-2022-0137.
[64] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst., vol. 28, pp. 91–99, 2015.
[65] L. Attard, C. J. Debono, G. Valentino, M. Di Castro, A. Masi, and L. Scibile, “Automatic Crack Detection using Mask R-CNN,” in 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Sep. 2019, pp. 152–157. doi: 10.1109/ISPA.2019.8868619.
[66] T. S. Tran, V. P. Tran, H. J. Lee, J. M. Flores, and V. P. Le, “A two-step sequential automated crack detection and severity classification process for asphalt pavements,” Int. J. Pavement Eng., vol. 23, no. 6, pp. 2019–2033, May 2022, doi: 10.1080/10298436.2020.1836561.
[67] Wang, P., Wang, C., Liu, H., Liang, M., Zheng, W., Wang, H., Zhu, S., Zhong, G. and Liu, “Research on Automatic Pavement Crack Recognition Based on the Mask R-CNN Model,” Coatings, vol. 13, no. 2, Art. no. 2, Feb. 2023, doi: 10.3390/coatings13020430.
[68] Dong, J., Liu, J., Fang, H., Zhang, J., Hu, H. and Ma, D., “Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on modified Mask R-CNN Algorithm,” Comput. Model. Eng. Sci., vol. 128, no. 2, pp. 541–564, Aug. 2021, doi: 10.32604/cmes.2021.015875.
[69] Y. Zhang, B. Chen, J. Wang, J. Li, and X. Sun, “APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation,” IEEE Access, vol. 8, pp. 199159–199170, 2020, doi: 10.1109/ACCESS.2020.3033661.
70
[70] Liu, Z., Yeoh, J.K., Gu, X., Dong, Q., Chen, Y., Wu, W., Wang, L. and Wang, D., “Automatic pixel-level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R-CNN,” Autom. Constr., vol. 146, 104689, Feb. 2023, doi: 10.1016/j.autcon.2022.104689.
[71] J. Zhai, Z. Sun, J. Huyan, H. Yang, and W. Li, “Automatic pavement crack detection using multimodal features fusion deep neural network,” Int. J. Pavement Eng., vol. 0, no. 0, pp. 1–14, Jun. 2022, doi: 10.1080/10298436.2022.2086692.
[72] Xiao, L., Li, W., Deng, N., Yuan, B., Bi, Y., Cui, Y. and Cui, X., “Automatic Pavement Crack Identification Based on an Improved C-Mask Region-Based Convolutional Neural Network Model,” Transp. Res. Rec., vol. 2677, no. 3, pp. 1194–1216, Mar. 2023, doi: 10.1177/03611981221122778.
[73] Y. Yang, Z. Zhao, L. Su, Y. Zhou, and H. Li, “Research on Pavement Crack Detection Algorithm based on Deep Residual Unet Neural Network,” J. Phys. Conf. Ser., vol. 2278, no. 1, 012020, May 2022, doi: 10.1088/1742-6596/2278/1/012020.
[74] L. Zhang, J. Shen, and B. Zhu, “A research on an improved Unet-based concrete crack detection algorithm,” Struct. Health Monit., vol. 20, no. 4, pp. 1864–1879, Jul. 2021, doi: 10.1177/1475921720940068.
[75] J. Huyan, W. Li, S. Tighe, Z. Xu, and J. Zhai, “CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection,” Struct. Control Health Monit., vol. 27, no. 8, e2551, 2020, doi: 10.1002/stc.2551.
[76] L. Fan, H. Zhao, Y. Li, S. Li, R. Zhou, and W. Chu, “RAO-UNet: a residual attention and octave UNet for road crack detection via balance loss,” IET Intell. Transp. Syst., vol. 16, no. 3, pp. 332–343, 2022, doi: 10.1049/itr2.12146.
[77] Chen, T., Cai, Z., Zhao, X., Chen, C., Liang, X., Zou, T. and Wang, P., “Pavement crack detection and recognition using the architecture of segNet,” J. Ind. Inf. Integr., vol. 18, 100144, 2020.
[78] G. Li, Q. Liu, W. Ren, W. Qiao, B. Ma, and J. Wan, “Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field,” Measurement, vol. 170, p. 108693, Jan. 2021, doi: 10.1016/j.measurement.2020.108693.
[79] K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” presented at the Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2961–2969. Accessed: Jan. 27, 2023. [Online]. Available: https://openaccess.thecvf.com/content_iccv_2017/html/He_Mask_R-CNN_ICCV_2017_paper.html
[80] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds.,
71
in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.
[81] F. Liu and L. Wang, “UNet-based model for crack detection integrating visual explanations,” Constr. Build. Mater., vol. 322, 126265, Mar. 2022, doi: 10.1016/j.conbuildmat.2021.126265.
[82] L. Wang, X. MA, and Y. Ye, “Computer vision-based Road Crack Detection Using an Improved I-UNet Convolutional Networks,” in 2020 Chinese Control And Decision Conference (CCDC), Aug. 2020, pp. 539–543. doi: 10.1109/CCDC49329.2020.9164476.
[83] G. Yu, J. Dong, Y. Wang, and X. Zhou, “RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation,” Sensors, vol. 23, no. 1, Art. no. 1, Jan. 2023, doi: 10.3390/s23010053.
[84] C. Han, T. Ma, J. Huyan, X. Huang, and Y. Zhang, “CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 22135–22144, Nov. 2022, doi: 10.1109/TITS.2021.3095507.
[85] J. Ha, D. Kim, and M. Kim, “Assessing severity of road cracks using deep learning-based segmentation and detection,” The Journal of Supercomputing, vol. 78, no. 16, pp. 17721–17735, May 2022, doi: 10.1007/s11227-022-04560-x.
[86] F. Liu, J. Liu, and L. Wang, “Asphalt Pavement Crack Detection Based on Convolutional Neural Network and Infrared Thermography,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 22145–22155, Nov. 2022, doi: 10.1109/TITS.2022.3142393.
[87] A. Issa, H. Samaneh, and M. Ghanim, “Predicting pavement condition index using artificial neural networks approach,” Ain Shams Eng. J., vol. 13, no. 1, 101490, Jan. 2022, doi: 10.1016/j.asej.2021.04.033.
[88] K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection,” Constr. Build. Mater., vol. 157, pp. 322–330, Dec. 2017, doi: 10.1016/j.conbuildmat.2017.09.110.
[89] S. Park, S. Bang, H. Kim, and H. Kim, “Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks,” J. Comput. Civ. Eng., vol. 33, no. 3, 04019017, May 2019, doi: 10.1061/(ASCE)CP.1943-5487.0000831.
[90] S. Li and X. Zhao, “Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique,” Adv. Civ. Eng., vol. 2019, e6520620, Apr. 2019, doi: 10.1155/2019/6520620.
[91] K. Chen, A. Yadav, A. Khan, Y. Meng, and K. Zhu, “Improved Crack Detection and Recognition Based on Convolutional Neural Network,” Model. Simul. Eng., vol. 2019, e8796743, Oct. 2019, doi: 10.1155/2019/8796743.
72
[92] B. Li, K. C. P. Wang, A. Zhang, E. Yang, and G. Wang, “Automatic classification of pavement crack using deep convolutional neural network,” Int. J. Pavement Eng., vol. 21, no. 4, pp. 457–463, Mar. 2020, doi: 10.1080/10298436.2018.1485917.
[93] Schmugge, S.J., Rice, L., Nguyen, N.R., Lindberg, J., Grizzi, R., Joffe, C. and Shin, M.C., “Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Mar. 2016, pp. 1–7. doi: 10.1109/WACV.2016.7477601.
[94] A. Dipankar and S. K. Suman, “Pavement crack detection based on a deep learning approach and visualisation by using GIS,” Int. J. Pavement Eng., vol. 24, no. 1, 2173754, Dec. 2023, doi: 10.1080/10298436.2023.2173754.
[95] J. C. H. Ong, M.-Z. P. Ismadi, and X. Wang, “A hybrid method for pavement crack width measurement,” Measurement, vol. 197, 111260, Jun. 2022, doi: 10.1016/j.measurement.2022.111260.
[96] Fan, Z., Li, C., Chen, Y., Di Mascio, P., Chen, X., Zhu, G. and Loprencipe, G., “Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement,” Coatings, vol. 10, no. 2, Art. no. 2, Feb. 2020, doi: 10.3390/coatings10020152.
[97] L. Gao, Y. Yu, Y. Hao Ren, and P. Lu, “Detection of Pavement Maintenance Treatments using Deep-Learning Network,” Transp. Res. Rec., vol. 2675, no. 9, pp. 1434–1443, Sep. 2021, doi: 10.1177/03611981211007846.
[98] H. Zhang, Z. Qian, Y. Tan, Y. Xie, and M. Li, “Investigation of pavement crack detection based on deep learning method using weakly supervised instance segmentation framework,” Constr. Build. Mater., vol. 358, 129117, Dec. 2022, doi: 10.1016/j.conbuildmat.2022.129117.
[99] Y. Inoue and H. Nagayoshi, “Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors,” in 2020 25th International Conference on Pattern Recognition (ICPR), Jan. 2021, pp. 65–72. doi: 10.1109/ICPR48806.2021.9412041.
[100] Y. Tang, Y. Qian, and E. Yang, “Weakly supervised convolutional neural network for pavement crack segmentation,” Intell. Transp. Infrastruct., vol. 1, p. liac013, Sep. 2022, doi: 10.1093/iti/liac013.
[101] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning Deep Features for Discriminative Localization,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921–2929. Accessed: Jul. 10, 2022. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Learning_Deep_Features_CVPR_2016_paper.html
73
[102] Z. Dong, J. Wang, B. Cui, D. Wang, and X. Wang, “Patch-based weakly supervised semantic segmentation network for crack detection,” Constr. Build. Mater., vol. 258, 120291, Oct. 2020, doi: 10.1016/j.conbuildmat.2020.120291.
[103] S. Huang, W. Tang, G. Huang, L. Huangfu, and D. Yang, “Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild,” IEEE Trans. Intell. Transp. Syst., pp. 1–13, 2023, doi: 10.1109/TITS.2023.3245192.
[104] Z. Al-Huda, B. Peng, R. N. A. Algburi, S. Alfasly, and T. Li, “Weakly supervised pavement crack semantic segmentation based on multi-scale object localization and incremental annotation refinement,” Appl. Intell., Oct. 2022, doi: 10.1007/s10489-022-04212-w.
[105] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. Accessed: Jan. 26, 2023. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
[106] A. Tao, K. Sapra, and B. Catanzaro, “Hierarchical Multi-Scale Attention for Semantic Segmentation.” arXiv, May 21, 2020. doi: 10.48550/arXiv.2005.10821.
[107] “NVIDIA/semantic-segmentation.” NVIDIA Corporation, Jan. 24, 2023. Accessed: Jan. 27, 2023. [Online]. Available: https://github.com/NVIDIA/semantic-segmentation
[108] O. Ronneberger, “Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation,” Bildverarbeitung für die Medizin 2017, pp. 3–3, 2017, doi: 10.1007/978-3-662-54345-0_3.
[109] J. Gildenblat, “Class Activation Map methods implemented in Pytorch.” Jul. 25, 2022. Accessed: Jul. 25, 2022. [Online]. Available: https://github.com/jacobgil/pytorch-grad-cam
[110] E. N. Mortensen and W. A. Barrett, “Interactive Segmentation with Intelligent Scissors,” Graph. Models Image Process., vol. 60, no. 5, pp. 349–384, Sep. 1998, doi: 10.1006/gmip.1998.0480.
[111] “U-Net: Semantic segmentation with PyTorch.” Mar. 18, 2023. Accessed: Mar. 18, 2023. [Online]. Available: https://github.com/milesial/Pytorch-UNet
[112] X. Yang and J. Yan, “Arbitrary-Oriented Object Detection with Circular Smooth Label,” in Computer Vision – ECCV 2020, A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds., Cham: Springer International Publishing, 2020, pp. 677–694. doi: 10.1007/978-3-030-58598-3_40.
[113] “Yolov5 for Oriented Object Detection.” Mar. 17, 2022. Accessed: Mar. 17, 2022. [Online]. Available: https://github.com/hukaixuan19970627/yolov5_obb
[114] G. Jocher, “YOLOv5 by Ultralytics.” May 2020. doi: 10.5281/zenodo.3908559.
74
[115] J. C. H. Ong, M.-Z. P. Ismadi, and X. Wang, “A hybrid method for pavement crack width measurement,” Measurement, vol. 197, 111260, Jun. 2022, doi: 10.1016/j.measurement.2022.111260.
[116] W. Wang, A. Zhang, K. C. Wang, A. F. Braham, and S. Qiu, “Pavement crack width measurement based on Laplace’s equation for continuity and unambiguity,” Comput.-Aided Civ. Infrastruct. Eng., vol. 33, no. 2, pp. 110–123, 2018.
[117] “Rival Solutions – Road Evaluation Simplified & Mobile.” http://www.rivalsolutions.com/rubix/#contact (accessed Jul. 28, 2023).
[118] R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2003
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