[1]

Yao Y, Tung STE, Glišić B. 2014. Crack detection and characterization techniques—an overview. Structural Control and Health Monitoring 21:1387−413

doi: 10.1002/stc.1655
[2]

Liu J, Gu J, Luo S. 2022. Research on road crack detection based on machine vision. 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), October 3−5, 2022, Beijing, China. USA: IEEE. pp. 543−47. doi: 10.1109/IAEAC54830.2022.9929645

[3]

Koch C, Georgieva K, Kasireddy V, Akinci B, Fieguth P. 2015. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics 29:196−210

doi: 10.1016/j.aei.2015.01.008
[4]

Labudzki R, Legutko S, Raos P. 2014. The essence and applications of machine vision. Tehnicki Vjesnik [Technical Gazette] 21:903−9

[5]

Abdel-Qader I, Abudayyeh O, Kelly ME. 2003. Analysis of edge-detection techniques for crack identification in bridges. Journal of Computing in Civil Engineering 17:255−63

doi: 10.1061/(asce)0887-3801(2003)17:4(255)
[6]

Kamaliardakani M, Sun L, Ardakani MK. 2016. Sealed-crack detection algorithm using heuristic thresholding approach. Journal of Computing in Civil Engineering 30:04014110

doi: 10.1061/(asce)cp.1943-5487.0000447
[7]

Li Q, Zou Q, Zhang D, Mao Q. 2011. FoSA: F* Seed-growing Approach for crack-line detection from pavement images. Image and Vision Computing 29:861−72

doi: 10.1016/j.imavis.2011.10.003
[8]

Guo WY, Wang XF, Xia XZ. 2014. Two-dimensional Otsu' s thresholding segmentation method based on grid box filter. Optik 125:5234−40

doi: 10.1016/j.ijleo.2014.05.003
[9]

Kanopoulos N, Vasanthavada N, Baker RL. 1988. Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-State Circuits 23:358−67

doi: 10.1109/4.996
[10]

Wang D, Zhou S. 2008. Color image recognition method based on the prewitt operator. 2008 International Conference on Computer Science and Software Engineering, December 12−14, 2008, Wuhan, China. USA: IEEE. pp. 170−73. doi: 10.1109/CSSE.2008.567

[11]

Li ES, Zhu SL, Zhu BS, Zhao Y, Xia CG, et al. 2009. An adaptive edge-detection method based on the canny operator. 2009 International Conference on Environmental Science and Information Application Technology, July 4−5, 2009, Wuhan, China. USA: IEEE. pp. 465−69. doi: 10.1109/ESIAT.2009.49

[12]

Cao W, Liu Q, He Z. 2020. Review of pavement defect detection methods. IEEE Access 8:14531−44

doi: 10.1109/ACCESS.2020.2966881
[13]

Sinha SK, Fieguth PW. 2006. Morphological segmentation and classification of underground pipe images. Machine Vision and Applications 17:21−31

doi: 10.1007/s00138-005-0012-0
[14]

Fujita Y, Hamamoto Y. 2011. A robust automatic crack detection method from noisy concrete surfaces. Machine Vision and Applications 22:245−54

doi: 10.1007/s00138-009-0244-5
[15]

Azouz Z, Honarvar Shakibaei Asli B, Khan M. 2023. Evolution of crack analysis in structures using image processing technique: a review. Electronics 12:3862

doi: 10.3390/electronics12183862
[16]

Zhu J, Zhong J, Ma T, Huang X, Zhang W, et al. 2022. Pavement distress detection using convolutional neural networks with images captured via UAV. Automation in Construction 133:103991

doi: 10.1016/j.autcon.2021.103991
[17]

Ayenu-Prah A, Attoh-Okine N. 2008. Evaluating pavement cracks with bidimensional empirical mode decomposition. EURASIP Journal on Advances in Signal Processing 2008:861701

doi: 10.1155/2008/861701
[18]

Vivekananthan V, Vignesh R, Vasanthaseelan S, Joel E, Kumar KS. 2023. Concrete bridge crack detection by image processing technique by using the improved OTSU method. Materials Today: Proceedings 74:1002−7

doi: 10.1016/j.matpr.2022.11.356
[19]

Li Y, Yang N. 2023. An improved crack identification method for asphalt concrete pavement. Applied Sciences 13:8696

doi: 10.3390/app13158696
[20]

Liu F, Liu J, Wang L. 2022. Asphalt pavement crack detection based on convolutional neural network and infrared thermography. IEEE Transactions on Intelligent Transportation Systems 23:22145−55

doi: 10.1109/TITS.2022.3142393
[21]

Eskandari Torbaghan M, Li W, Metje N, Burrow M, Chapman DN, et al. 2020. Automated detection of cracks in roads using ground penetrating radar. Journal of Applied Geophysics 179:104118

doi: 10.1016/j.jappgeo.2020.104118
[22]

Chapeleau X, Blanc J, Hornych P, Gautier JL, Carroget J. 2014. Use of distributed fiber optic sensors to detect damage in a pavement. In Asphalt Pavements, ed. Kim YR. 1st Edition. London: CRC Press. doi: 10.1201/b17219-60

[23]

Xu W, Tang ZM, Xu D, Wu GX. 2015. Integrating multi-features fusion and gestalt principles for pavement crack detection. Journal of Computer-Aided Design & Computer Graphics 27(1):147−56

[24]

Zhang Y, Zhou H. 2012. Automatic pavement cracks detection and classification using radon transform. Journal of Information and Computational Science 9:5241−7

[25]

Zhang A, Li QJ, Wang KCP, Qiu S. 2013. Matched filtering algorithm for pavement cracking detection. Transportation Research Record: Journal of the Transportation Research Board 2367:30−42

doi: 10.3141/2367-04
[26]

Sun X, Huang J, Liu W, Xu M. 2012. Pavement crack characteristic detection based on sparse representation. EURASIP Journal on Advances in Signal Processing 2012:191

doi: 10.1186/1687-6180-2012-191
[27]

Stentoumis C, Protopapadakis E, Doulamis A, Doulamis N. 2016. A holistic approach for inspection of civil infrastructures based on computer vision techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial lnformation Sciences, 2016 XXlll ISPRS Congress,12−19 July 2016, Prague, Czech Republic. Volume XL1-B5. pp. 131−38. doi: 10.5194/isprsarchives-xli-b5-131-2016

[28]

Ni T, Zhou R, Gu C, Yang Y. 2020. Measurement of concrete crack feature with Android smartphone APP based on digital image processing techniques. Measurement 150:107093

doi: 10.1016/j.measurement.2019.107093
[29]

Hoang ND, Nguyen QL. 2018. Metaheuristic optimized edge detection for recognition of concrete wall cracks: a comparative study on the performances of Roberts, prewitt, canny, and sobel algorithms. Advances in Civil Engineering 2018:7163580

doi: 10.1155/2018/7163580
[30]

Hu C, He L, Tao J, Wang M, Zhang D. 2022. Asphalt pavement crack detection based on fusion of neighborhood and gradient salient features. Journal of Computer-Aided Design & Computer Graphics 34:245−53

doi: 10.3724/sp.j.1089.2022.18891
[31]

Talab AMA, Huang Z, Xi F, Liu H. 2016. Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik 127:1030−33

doi: 10.1016/j.ijleo.2015.09.147
[32]

Ouyang A, Luo C, Zhou C. 2011. Surface distresses detection of pavement based on digital image processing. In Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, 2011. vol 347. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 368−75. doi: 10.1007/978-3-642-18369-0_42

[33]

Kumare JS, Gupta P, Singh UP, Singh RK. 2019. An efficient contrast enhancement technique based on firefly optimization. In Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, eds. Ray K, Sharma T, Rawat S, Saini R, Bandyopadhyay A. vol 742. Singapore: Springer. pp. 181−92. doi: 10.1007/978-981-13-0589-4_17

[34]

Li G, Xu Y, Li J. 2013. Fuzzy contrast enhancement algorithm for road surface image based on adaptively changing index via grey entropy. Information Technology Journal 12:5309−14

doi: 10.3923/itj.2013.5309.5314
[35]

Yao M, Zhao Z, Yao X, Xu B. 2015. Fusing complementary images for pavement cracking measurements. Measurement Science and Technology 26:025005

doi: 10.1088/0957-0233/26/2/025005
[36]

Ashraf A, Sophian A, Shafie AA, Gunawan TS, Ismail NN. 2023. Machine learning-based pavement crack detection, classification, and characterization: a review. Bulletin of Electrical Engineering and Informatics 12:3601−19

doi: 10.11591/eei.v12i6.5345
[37]

Boubenna H, Lee D. 2018. Image-based emotion recognition using evolutionary algorithms. Biologically Inspired Cognitive Architectures 24:70−76

doi: 10.1016/j.bica.2018.04.008
[38]

Zhou D, Shen X, Dong W. 2012. Image zooming using directional cubic convolution interpolation. IET Image Processing 6:627−34

doi: 10.1049/iet-ipr.2011.0534
[39]

Sobel I. 1970. Camera Models and Machine Perception. USA: Stanford University.

[40]

Marr DC, Hildreth EC. 1980. Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences 207:187−217

doi: 10.1098/rspb.1980.0020
[41]

Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI- 8:679−98

doi: 10.1109/TPAMI.1986.4767851
[42]

Al-amri SS, Kalyankar NV, Khamitkar SD. 2010. Image segmentation by using edge detection. International Journal on Computer Science and Engineering 2:804

[43]

Ding L, Goshtasby A. 2001. On the canny edge detector. Pattern Recognition 34:721−25

doi: 10.1016/S0031-3203(00)00023-6
[44]

Xu Q, Varadarajan S, Chakrabarti C, Karam LJ. 2014. A distributed Canny edge detector: algorithm and FPGA implementation. IEEE Transactions on Image Processing 23:2944−60

doi: 10.1109/tip.2014.2311656
[45]

Jing J, Liu S, Liu C, Gao T, Zhang W, et al. 2021. A novel decision mechanism for image edge detection. In Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, eds. Huang DS, Jo KH, Li J, Gribova V, Bevilacqua V. Cham: Springer. pp. 274−87. doi: 10.1007/978-3-030-84522-3_22

[46]

Zhang W, Zhao Y, Breckon TP, Chen L. 2017. Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognition 63:193−205

doi: 10.1016/j.patcog.2016.10.008
[47]

Ma C, Wang W, Zhao C, Di F, Zhu Z. 2009. Pavement cracks detection based on FDWT. 2009 International Conference on Computational Intelligence and Software Engineering, December 11−13, 2009, Wuhan, China. USA: IEEE. pp. 1−4. doi: DOI: 10.1109/CISE.2009.5362561

[48]

Ragnoli A, De Blasiis MR, Di Benedetto A. 2018. Pavement distress detection methods: a review. Infrastructures 3:58

doi: 10.3390/infrastructures3040058
[49]

Min J. 2018. Measurement method of screw thread geometric error based on machine vision. Measurement and Control 51:304−10

doi: 10.1177/0020294018786751
[50]

Wang Y, Zhang JY, Liu JX, Zhang Y, Chen ZP, et al. 2019. Research on crack detection algorithm of the concrete bridge based on image processing. Procedia Computer Science 154:610−16

doi: 10.1016/j.procs.2019.06.096
[51]

Zhao H, Qin G, Wang X. 2010. Improvement of canny algorithm based on pavement edge detection. 2010 3 rd International Congress on Image and Signal Processing. October 16−18, 2010, Yantai, China. USA: IEEE. pp. 964−67. doi: 10.1109/CISP.2010.5646923

[52]

Huang M, Liu Y, Yang Y. 2022. Edge detection of ore and rock on the surface of explosion pile based on improved Canny operator. Alexandria Engineering Journal 61:10769−77

doi: 10.1016/j.aej.2022.04.019
[53]

Sridevi M, Mala C. 2012. A survey on monochrome image segmentation methods. Procedia Technology 6:548−55

doi: 10.1016/j.protcy.2012.10.066
[54]

Salari E, Bao G. 2011. Pavement distress detection and severity analysis. Image Processing: Machine Vision Applications IV, San Francisco Airport, 2011, California, USA. SPIE. doi: 10.1117/12.876724

[55]

Huang Y, Tsai YJ. 2011. Dynamic programming and connected component analysis for an enhanced pavement distress segmentation algorithm. Transportation Research Record 2225:89−98

doi: 10.3141/2225-10
[56]

Shao C, Chen Y, Xu F, Wang S. 2019. A kind of pavement crack detection method based on digital image processing. 2019 IEEE 4 th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), December 20−22, 2019, Chengdu, China. USA: IEEE. pp. 397−401. doi: 10.1109/IAEAC47372.2019.8997810

[57]

Basavaprasad B, Ravi M. 2014. A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology 3:310−15

doi: 10.15623/ijret.2014.0315060
[58]

Zhao F, Chao Y, Liu X, Li L. 2022. A novel crack segmentation method based on morphological-processing network. 2022 15 th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), November 5−7, 2022, Beijing, China. USA: IEEE. pp. 1−6. doi: DOI: 10.1109/CISP-BMEI56279.2022.9980022

[59]

Varadharajan S, Jose S, Sharma K, Wander L, Mertz C. 2014. Vision for road inspection. IEEE Winter Conference on Applications of Computer Vision, March 24−26, 2014, Steamboat Springs, CO, USA. USA: IEEE. pp. 115−22. doi: 10.1109/WACV.2014.6836111

[60]

Nguyen A, Gharehbaghi V, Le NT, Sterling L, Chaudhry UI, et al. 2023. ASR crack identification in bridges using deep learning and texture analysis. Structures 50:494−507

doi: 10.1016/j.istruc.2023.02.042
[61]

Park SE, Eem SH, Jeon H. 2020. Concrete crack detection and quantification using deep learning and structured light. Construction and Building Materials 252:119096

doi: 10.1016/j.conbuildmat.2020.119096
[62]

Tran TS, Nguyen SD, Lee HJ, Tran VP. 2023. Advanced crack detection and segmentation on bridge decks using deep learning. Construction and Building Materials 400:132839

doi: 10.1016/j.conbuildmat.2023.132839
[63]

Nguyen SD, Tran TS, Tran VP, Lee HJ, Piran MJ, et al. 2023. Deep learning-based crack detection: a survey. International Journal of Pavement Research and Technology 16:943−67

doi: 10.1007/s42947-022-00172-z
[64]

Soukup D, Huber-Mörk R. 2014. Convolutional neural networks for steel surface defect detection from photometric stereo images. In Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, eds. Bebis G, et al. 2014. Cham: Springer. pp. 668−77. doi: 10.1007/978-3-319-14249-4_64

[65]

Cha YJ, Choi W, Büyüköztürk O. 2017. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering 32:361−78

doi: 10.1111/mice.12263
[66]

Maeda H, Sekimoto Y, Seto T, Kashiyama T, Omata H. 2018. Road damage detection using deep neural networks with images captured through a smartphone. arXiv Preprint

doi: 10.48550/arXiv.1801.09454
[67]

Yusof NM, Ibrahim A, Noor MM, Tahir NM, Yusof NM, et al. 2019. Deep convolution neural network for crack detection on asphalt pavement. Journal of Physics: Conference Series 1349:012020

doi: 10.1088/1742-6596/1349/1/012020
[68]

Shatnawi N. 2018. Automatic pavement cracks detection using image processing techniques and neural network. International Journal of Advanced Computer Science and Applications 9(9):399−402

doi: 10.14569/ijacsa.2018.090950
[69]

Yusof NAM, Osman MK, Noor MHM, Ibrahim A, Tahir NM, et al. 2018. Crack detection and classification in asphalt pavement images using deep convolution neural network. 2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), November 23−25, 2018, Penang, Malaysia. USA: IEEE. pp. 227−32. doi: 10.1109/ICCSCE.2018.8685007

[70]

Tsai Y, Jiang C, Wang Z. 2012. Pavement crack detection using high-resolution 3D line laser imaging technology. In 7 th RILEM International Conference on Cracking in Pavements, eds. Scarpas A, Kringos N, Al-Qadi IAL. Dordrecht, Netherlands: Springer. pp. 169−78. doi: 10.1007/978-94-007-4566-7_17

[71]

Wei Y, Wang Z, Xu M. 2017. Road structure refined CNN for road extraction in aerial image. IEEE Geoscience and Remote Sensing Letters 14:709−13

doi: 10.1109/LGRS.2017.2672734
[72]

Alshehhi R, Marpu PR, Woon WL, Dalla Mura M. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing 130:139−49

doi: 10.1016/j.isprsjprs.2017.05.002
[73]

Henry C, Azimi SM, Merkle N. 2018. Road segmentation in SAR satellite images with deep fully convolutional neural networks. IEEE Geoscience and Remote Sensing Letters 15:1867−71

doi: 10.1109/LGRS.2018.2864342
[74]

Xie Y, Miao F, Zhou K, Peng J. 2019. HsgNet: a road extraction network based on global perception of high-order spatial information. ISPRS International Journal of Geo-Information 8:571

doi: 10.3390/ijgi8120571
[75]

Cheng G, Wang Y, Xu S, Wang H, Xiang S, et al. 2017. Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing 55:3322−37

doi: 10.1109/TGRS.2017.2669341
[76]

Shi Q, Liu X, Li X. 2017. Road detection from remote sensing images by generative adversarial networks. IEEE Access 6:25486−94

doi: 10.1109/ACCESS.2017.2773142
[77]

Zhou L, Zhang C, Wu M. 2018. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 18−22, 2018, Salt Lake City, UT, USA. USA: IEEE. pp. 192−1924. doi: 10.1109/CVPRW.2018.00034

[78]

Doshi J. 2018. Residual inception skip network for binary segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 18−22, 2018, Salt Lake City, UT, USA. USA: IEEE. pp. 206−2063. doi: 10.1109/CVPRW.2018.00037

[79]

Naik SK, Murthy CA. 2003. Hue-preserving color image enhancement without gamut problem. IEEE Transactions on Image Processing 12:1591−98

doi: 10.1109/TIP.2003.819231
[80]

Pitas I, Kiniklis P. 1996. Multichannel techniques in color image enhancement and modeling. IEEE Transactions on Image Processing 5:168−71

doi: 10.1109/83.481684
[81]

Buzuloiu V, Ciuc M, Rangayyan R, Vertan C. 2001. Adaptive-neighborhood histogram equalization of color images. Journal of Electronic Imaging 10:445

doi: 10.1117/1.1353200
[82]

Trahanias PE, Venetsanopoulos AN. 2002. Color image enhancement through 3-D histogram equalization. Proceedings 11 th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis, August 30 − September 1, 1992, The Hague, Netherlands. USA: IEEE. pp. 545−48. doi: 10.1109/ICPR.1992.202045

[83]

Yang CC, Rodrı́guez JJ. 1997. Efficient luminance and saturation processing techniques for color images. Journal of Visual Communication and Image Representation 8:263−77

doi: 10.1006/jvci.1997.0342
[84]

Rodr'Iguez JJ, Yang CC. 1999. Saturation clipping in the LHS and YIQ color spaces. Proceedings of SPIE - The International Society for Optical Engineering 2658

[85]

Dung CV, Anh LD. 2019. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction 99:52−58

doi: 10.1016/j.autcon.2018.11.028
[86]

Liu Z, Cao Y, Wang Y, Wang W. 2019. Computer vision-based concrete crack detection using U-Net fully convolutional networks. Automation in Construction 104:129−39

doi: 10.1016/j.autcon.2019.04.005
[87]

Wang X, Hu Z. 2017. Grid-based pavement crack analysis using deep learning. 2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8−10, 2017, Banff, AB, Canada. US: IEEE. pp. 917−24. doi: 10.1109/ICTIS.2017.8047878

[88]

Ahmed TU, Shahadat Hossain M, Alam MJ, Andersson K. 2019. An integrated CNN-RNN framework to assess road crack. 2019 22 nd International Conference on Computer and Information Technology (ICCIT), December 18−20, 2019, Dhaka, Bangladesh. USA: IEEE. pp. 1−6. doi: 10.1109/ICCIT48885.2019.9038607

[89]

Xu G, Xu G. 2023. Using a CNN to solve the problem of asphalt pavement crack detection. Proceedings of the 2023 15 th International Conference on Machine Learning and Computing. February 17−20, 2023, Zhuhai, China. New York, USA: ACM. pp. 290−97. doi: 10.1145/3587716.3587764

[90]

Yang N, Li Y, Ma R. 2022. An efficient method for detecting asphalt pavement cracks and sealed cracks based on a deep data-driven model. Applied Sciences 12:10089

doi: 10.3390/app121910089
[91]

Han Z, Chen H, Liu Y, Li Y, Du Y, et al. 2021. Vision-based crack detection of asphalt pavement using deep convolutional neural network. Iranian Journal of Science and Technology, Transactions of Civil Engineering 45:2047−55

doi: 10.1007/s40996-021-00668-x
[92]

Jiang J, Li P, Wang J, Chen H, Zhang T. 2024. Asphalt pavement crack detection based on infrared thermography and deep learning. International Journal of Pavement Engineering 25:2295906

doi: 10.1080/10298436.2023.2295906
[93]

Yang Z, Ni C, Li L, Luo W, Qin Y. 2022. Three-stage pavement crack localization and segmentation algorithm based on digital image processing and deep learning techniques. Sensors 22:8459

doi: 10.3390/s22218459
[94]

He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27−30, 2016, Las Vegas, NV, USA. USA: IEEE. pp. 770−78. doi: 10.1109/CVPR.2016.90

[95]

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, et al. 2017. MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv Preprint

doi: 10.48550/arXiv.1704.04861
[96]

Zhang X, Zhou X, Lin M, Sun J. 2018. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18−23, 2018, Salt Lake City, UT, USA. USA: IEEE. pp. 6848−56. doi: 10.1109/CVPR.2018.00716

[97]

Shin HC, Roth HR, Gao M, Lu L, Xu Z, et al. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35:1285−98

doi: 10.1109/TMI.2016.2528162
[98]

Shorten C, Khoshgoftaar TM. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6:60

doi: 10.1186/s40537-019-0197-0
[99]

Kestur R, Farooq S, Abdal R, Mehraj E, Narasipura O, et al. 2018. UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle. Journal of Applied Remote Sensing 12(1):016020

doi: 10.1117/1.jrs.12.016020
[100]

Panboonyuen T, Vateekul P, Jitkajornwanich K, Lawawirojwong S. 2017. An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery. In Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, eds. Meesad P, Sodsee S, Unger H. Cham: Springe. pp. 191−201. doi: 10.1007/978-3-319-60663-7_18

[101]

Xu H, Su X, Wang Y, Cai H, Cui K, et al. 2019. Automatic bridge crack detection using a convolutional neural network. Applied Sciences 9:2867

doi: 10.3390/app9142867
[102]

Yu Z, Shen Y, Sun Z, Chen J, Gang W. 2022. Cracklab: a high-precision and efficient concrete crack segmentation and quantification network. Developments in the Built Environment 12:100088

doi: 10.1016/j.dibe.2022.100088
[103]

Fuentes R, Pauly L, Peel H, Luo S, Hogg D. 2017. Deeper networks for pavement crack detection. Proceedings of the 34 th International Symposium on Automation and Robotics in Construction (ISARC), Taipei, Taiwan. The International Association for Automation and Robotics in Construction. pp. 479−85. doi: 10.22260/ISARC2017/0066

[104]

Deng L, Hinton G, Kingsbury B. 2013. New types of deep neural network learning for speech recognition and related applications: an overview. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 26−31, 2013, Vancouver, BC, Canada. USA: IEEE. pp. 8599−603. doi: 10.1109/ICASSP.2013.6639344

[105]

Li J, He Z, Li D, Zheng A. 2022. Research on water seepage detection technology of tunnel asphalt pavement based on deep learning and digital image processing. Scientific Reports 12:11519

doi: 10.1038/s41598-022-15828-w
[106]

Lei H, Cheng J, Xu Q. 2019. Cement pavement surface crack detection based on image processing. Mechanical Engineering Science 1(1):46−51

doi: 10.33142/me.v1i1.661
[107]

Nishikawa T, Yoshida J, Sugiyama T, Fujino Y. 2012. Concrete crack detection by multiple sequential image filtering. Computer-Aided Civil and Infrastructure Engineering 27:29−47

doi: 10.1111/j.1467-8667.2011.00716.x
[108]

Kim IH, Jeon H, Baek SC, Hong WH, Jung HJ. 2018. Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors 18:1881

doi: 10.3390/s18061881
[109]

Zhang K, Cheng HD, Zhang B. 2018. Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning. Journal of Computing in Civil Engineering 32:04018001

doi: 10.1061/(asce)cp.1943-5487.0000736
[110]

Shim S, Kim J, Cho GC, Lee SW. 2020. Multiscale and adversarial learning-based semi-supervised semantic segmentation approach for crack detection in concrete structures. IEEE Access 8:170939−50

doi: 10.1109/ACCESS.2020.3022786
[111]

Li G, Wan J, He S, Liu Q, Ma B. 2020. Semi-supervised semantic segmentation using adversarial learning for pavement crack detection. IEEE Access 8:51446−59

doi: 10.1109/ACCESS.2020.2980086