REFERENCES
1. de Langen P, Turró M, Fontanet M, Caballé J. The infrastructure investment needs and financing challenge of European ports; 2018. Available from: https://www.espo.be/media/Port%20Investment%20Study%202018_FINAL_1.pdf[Last accessed on 11 Nov 2024].
2. Lauritzen PN, Reichard J, Ahmed S, Safa M. Review of non-destructive testing methods for physical condition monitoring in the port industry. J Constr Eng Manag Innov 2019;2:103-11.
3. Hossain NUI, Nur F, Hosseini S, Jaradat R, Marufuzzaman M, Puryear SM. A Bayesian network based approach for modeling and assessing resilience: a case study of a full service deep water port. Reliab Eng Syst Saf 2019;189:378-96.
4. Zhang Y, Kim CW, Tee KF, Lam JSL. Optimal sustainable life cycle maintenance strategies for port infrastructures. J Clean Prod 2017;142:1693-709.
6. Negi P, Kromanis R, Dorée A, Wijnberg KM. Structural health monitoring of inland navigation structures and ports: a review on developments and challenges. Struct Health Monit 2023;23:147592172311707.
8. Klerk WJ, Schweckendiek T, den Heijer F, Kok M. Value of information of structural health monitoring in asset management of flood defences. Infrastructures 2019;4:56.
9. Leblouba M, Tarabin M, Zahri M. Probabilistic analysis and simulation of crack propagation in concrete pavements and surfaces. Sci Rep 2022;12:14157.
10. Zhang Y, Lu Y, Duan Y, et al. Robust surface crack detection with structure line guidance. Int J Appl Earth Obs Geoinf 2023;124:103527.
11. Munawar HS, Hammad AWA, Haddad A, Soares CAP, Waller ST. Image-based crack detection methods: a review. Infrastructures 2021;6:115.
12. Gupta P, Dixit M. Image-based crack detection approaches: a comprehensive survey. Multimed Tools Appl 2022;81:40181-229.
13. Ding W, Yang H, Yu K, Shu J. Crack detection and quantification for concrete structures using UAV and transformer. Automat Constr 2023;152:104929.
14. Jin T, Zhang W, Chen C, Chen B, Zhuang Y, Zhang H. Deep-learning- and unmanned aerial vehicle-based structural crack detection in concrete. Buildings 2023;13:3114.
15. Song F, Liu B, Yuan G. Pixel-level crack identification for bridge concrete structures using unmanned aerial vehicle photography and deep learning. Struct Control Health Monit 2024;2024:1299095.
16. Li Y, Ma J, Zhao Z, Shi G. A novel approach for UAV image crack detection. Sensors 2022;22:3305.
17. Woo HJ, Seo DM, Kim MS, Park MS, Hong WH, Baek SC. Localization of cracks in concrete structures using an unmanned aerial vehicle. Sensors 2022;22:6711.
18. Choi D, Bell W, Kim D, Kim J. UAV-driven structural crack detection and location determination using convolutional neural networks. Sensors 2021;21:2650.
19. Tsaimou CN, Sartampakos P, Tsoukala VK. UAV-driven approach for assisting structural health monitoring of port infrastructure. Struct Infrastruct Eng 2023:1-20.
20. Ali R, Chuah JH, Talip MSA, Mokhtar N, Shoaib MA. Structural crack detection using deep convolutional neural networks. Autom Constr 2022;133:103989.
21. Arvidsson B, Johansson J, Guldåker N. Critical infrastructure, geographical information science and risk governance: a systematic crossfield review. Reliab Eng Syst Saf 2021;213:107741.
22. Lesiak P. Inspection and maintenance of railway infrastructure with the use of unmanned aerial vehicles. Probl Kolejn 2020:115-27.
23. Li R, Yu J, Li F, Yang R, Wang Y, Peng Z. Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN. Constr Build Mater 2023;362:129659.
24. Zhao Y, Zhou L, Wang X, Wang F, Shi G. Highway crack detection and classification using UAV remote sensing images based on CrackNet and CrackClassification. Appl Sci 2023;13:7269.
25. Azouz Z, Honarvar Shakibaei Asli B, Khan M. Evolution of crack analysis in structures using image processing technique: a review. Electronics 2023;12:3862.
26. Sundararajan D. Digital image processing: a signal processing and algorithmic approach Singapore: Springer; 2017.
27. Liu YF, Cho S, Spencer Jr. BF, Fan JS. Concrete crack assessment using digital image processing and 3D scene reconstruction. J Comput Civ Eng 2016;30:04014124.
28. Archana R, Jeevaraj PSE. Deep learning models for digital image processing: a review. Artif Intell Rev 2024;57:11.
29. Fan L, Zhang F, Fan H, Zhang C. Brief review of image denoising techniques. Vis Comput Ind Biomed Art 2019;2:7.
30. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62-6.
31. Niblack W. An introduction to digital image processing. Englewood Cliffs, NJ: Prentice-Hall International; 1986. Available from: https://archive.org/details/introductiontodi0000nibl[Last accessed on 11 Nov 2024].
32. Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognit 2000;33:225-36.
33. Sobel I, Feldman G. A 3×3 isotropic gradient operator for image processing; 1973. Available from: https://www.researchgate.net/publication/285159837_A_33_isotropic_gradient_operator_for_image_processing[Last accessed on 11 Nov 2024].
34. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986;PAMI-8:679-98.
35. Dorafshan S, Maguire M, Qi X. Automatic surface crack detection in concrete structures using OTSU thresholding and morphological operations. UTC Report 01-2016. Utah State University; 2016.
36. Kim IH, Jeon H, Baek SC, Hong WH, Jung HJ. Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors 2018;18:1881.
37. Li H, Wang W, Wang M, Li L, Vimlund V. A review of deep learning methods for pixel-level crack detection. J Traffic Transp Eng 2022;9:945-68.
38. Jofré-Briceño C, Muñoz-La Rivera F, Atencio E, Herrera RF. Implementation of facility management for port infrastructure through the use of UAVs, photogrammetry and BIM. Sensors 2021;21:6686.
39. Harris DK, Brooks CN, Ahlborn TM. Synthesis of field performance of remote sensing strategies for condition assessment of in-service bridges in Michigan. J Perform Constr Facil 2016;30:04016027.
41. Rasterio/affine’s documentation; 2024. Available from: https://affine.readthedocs.io/en/latest/[Last accessed on 11 Nov 2024].
42. Chen C, Seo H, Jun C, Zhao Y. A potential crack region method to detect crack using image processing of multiple thresholding. Signal Image Video Process 2022;16:1673-81.
43. Chityala R, Pudipeddi S. Image processing and acquisition using Python Boca Raton: Chapman and Hall/CRC Press; 2020. p. p. 452.
44. Owotogbe JS, Ibiyemi TS, Adu BA. A comprehensive review on various types of noise in image processing. Int J Sci Eng Res 2019;10: 388-93. Available from: https://www.ijser.org/researchpaper/A-COMPREHENSIVE-REVIEW-ON-VARIOUS-TYPES-OF-NOISE-IN-IMAGE-PROCESSING.pdf[Last accessed on 11 Nov 2024].
45. Gonzalez RC, Woods RE. Digital image processing. 4th ed. Essex, England: Pearson Education Limited; 2018. Available from: https://dl.icdst.org/pdfs/files4/01c56e081202b62bd7d3b4f8545775fb.pdf[Last accessed on 11 Nov 2024].
46. Xu B, Chen J, Yu P. Vectorization of classified remote sensing raster data to establish topological relations among polygons. Earth Sci Inform 2017;10:99-113.
47. Zhang F, Hu Z, Yang K, Fu Y, Feng Z, Bai M. The surface crack extraction method based on machine learning of image and quantitative feature information acquisition method. Remote Sens 2021;13:1534.
48. Obi JC. A comparative study of several classification metrics and their performances on data. World J Adv Eng Technol Sci 2023;8:308-14.
50. Yang YS, Wu Cl, Hsu TTC, Yang HC, Lu HJ, Chang CC. Image analysis method for crack distribution and width estimation for reinforced concrete structures. Autom Constr 2018;91:120-32.
51. Feng K, Ji JC, Ni Q, Li Y, Mao W, Liu L. A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system. Wear 2023;522:204697.
52. Feng K, Ni Q, Chen Y, Ge J, Liu Z. A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems. Struct Health Monit 2023;22: 147592172211470. Available from: https://www.researchgate.net/publication/367100031_A_cyclostationarity-based_wear_monitoring_framework_of_spur_gears_in_intelligent_manufacturing_systems[Last accessed on 11 Nov 2024].
