The assessment of steel bridge paint condition is often subjective because of its reliance on visual inspection methods. This study presents the development of an automated paint condition assessment (APCA) system designed for objective and quantitative evaluation of steel bridge paint condition. The APCA system integrates sensing, onboard processor, and unmanned mobile robot modules. The sensing module utilizes an invisible line laser beam to heat the inspection surface, while infrared and vision cameras capture thermal and vision images. The data processing module subsequently visualizes and quantifies the paint thickness and defects, providing automated paint condition ratings in accordance with current paint rating guidelines. The sensing module is mounted onto an unmanned mobile robot module equipped with magnetic wheels. The APCA system is deployed onto a steel bridge, where it is maneuvered across inspection areas using a remote controller. This study offers the following uniqueness: (1) automated detection, classification, and quantification of paint defects; (2) quantification and visualization of paint thickness; (3) capability to inspect areas that are difficult or hazardous for manual assessment; and (4) automatic paint condition rating aligned with existing paint rating guidelines. The performance of the proposed APCA system was validated through field bridge and specimen testing.
AthaDJJahanshahiMR.Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Struct Health Monit2018; 17(5): 1110–1128.
2.
HwangSKimHLimHJ, et al. Automated visualization of steel structure coating thickness using line laser scanning thermography. Autom Constr2022; 139: 104267.
3.
AlmeidaESantosDNUruchurtuJ.Corrosion performance of waterborne coatings for structural steel. Prog Org Coat1999; 37(3): 131–140.
4.
AdsettsJREbrahimiNZhangJ, et al. Steel bridge-coating systems and their environmental impacts: current practices and future trends. Coatings2023; 13(5): 850.
5.
ZhangDYuYTianG, et al. Thickness measurement of multi-layer conductive coatings using multifrequency eddy current techniques. Nondestruct Test Eval2016; 31(3): 191–208.
6.
AdewaleIDTianGY.Decoupling the influence of permeability and conductivity in pulsed eddy-current measurements. IEEE Trans Magnet2013; 49(3): 1119–1127.
7.
CaoBWangMLiX, et al. Noncontact thickness measurement of multilayer coatings on metallic substrate using pulsed terahertz technology. IEEE Sens J2020; 20(6): 3162–3171.
8.
KrimiSKlierJJonuscheitJ, et al. Highly accurate thickness measurement of multi-layered automotive paints using terahertz technology. Appl Phys Lett2016; 109(2): 021105.
9.
KetelsenHMästleRLiebermeisterL, et al. THz time-domain ellipsometer for material characterization and paint quality control with more than 5 THz bandwidth. Appl Sci2022; 12(8): 3744.
10.
YasudaTIwataTArakiT, et al. Improvement of minimum paint film thickness for THz paint meters by multiple-regression analysis. Appl Opt2007; 46(30): 7518–7526.
11.
CookDJLeeSSharpeSJ, et al. Accuracy and linearity of time-domain THz paint thickness measurements. In: Linden KJ and Sadwick LP(eds.) Terahertz technology and applications. San jose, CA: SPIE, 2008.
12.
YasudaTYasuiTArakiT, et al. Real-time two-dimensional terahertz tomography of moving objects. Opt Commun2006; 267(1): 128–136.
13.
HuynhCPMustaphaSRuncieP, et al. Multi-class support vector machines for paint condition assessment on the Sydney Harbour Bridge using hyperspectral imaging. Struct Monit Maint2015; 2(3): 181–197.
14.
LaoforCPeansupapV.Defect detection and quantification system to support subjective visual quality inspection via a digital image processing: a tiling work case study. Autom Constr2012; 24: 160–174.
15.
WuHLuoHZhuW, et al. Surface defect detection of plaster coating based on machine vision. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), 2017. Beijing, China: IEEE.
16.
HalabeUBVasudevanAKlinkhachornP, et al. Detection of subsurface defects in fiber reinforced polymer composite bridge decks using digital infrared thermography. Nondestruct Test Eval2007; 22(2–3): 155–175.
17.
FanLShiX.Techniques of corrosion monitoring of steel rebar in reinforced concrete structures: a review. Struct Health Monit2022; 21(4): 1879–1905.
18.
SakagamiT, Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges. Fatigue Fract Eng Mater Struct2015; 38(7): 755–779.
19.
Ministry of Land, Infrastructure and Transport. Special act on the safety control and maintenance of establishments. Republic of Korea: Ministry of Land, Infrastructure and Transport, 2021.
20.
KumarPSuprajaBSwamyNS, et al. Real-time concrete damage detection using deep learning for high rise structures. IEEE Access2021; 9: 112312–112331.
21.
JungH-JLeeJ-HKimI-H. Challenging issues and solutions of bridge inspection technology using unmanned aerial vehicles. In: SohnH (ed.) SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring. Vol. 10598. Denver, CO: SPIE, 2018.
22.
WhitleyTTomiczekATrippC, et al. Design of a small unmanned aircraft system for bridge inspections. Sensors (Basel)2020; 20(18): 5358.
23.
BenjumeaDAlcántaraARamosA, et al. Localization system for lightweight unmanned aerial vehicles in inspection tasks. Sensors (Basel)2021; 21(17): 5937.
24.
HwangSAnY-KSohnH.Continuous-wave line laser thermography for monitoring of rotating wind turbine blades. Struct Health Monit2019; 18(4): 1010–1021.
25.
HwangSAnY-KKimJ-M, et al. Monitoring and instantaneous evaluation of fatigue crack using integrated passive and active laser thermography. Opt Lasers Eng2019; 119: 9–17.
KimJASungJYParkSH. Comparison of faster-RCNN, YOLO, and SSD for real-time vehicle type recognition. In: 2020 IEEE international conference on consumer electronics—Asia (ICCE-Asia), Seoul, Republic of Korea, 1–03 November 2020. IEEE.
28.
RedmonJDivvalaSGirshickR, et al. You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, 27–30 June. 2016. IEEE.
29.
GirshickR. Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, Chile, 07–13 December 2015. IEEE.
30.
GirshickRDonahueJDarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, 23–28 June 2014. IEEE.
31.
RenSHeKGirshickR, et al. Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst2015; 28: 1137–1149.
32.
RedmonJFarhadiA.YOLOv3: an incremental improvement. 2018: arXiv:1804.02767.
33.
BochkovskiyAWangC-YLiaoH-YM. Yolov4: optimal speed and accuracy of object detection. arXiv Preprint 2020; 2020: arXiv:2004.10934.
34.
LiCLiLJiangH, et al. YOLOv6: a single-stage object detection framework for industrial applications. arXiv Preprint2022; 2022: arXiv:2209.02976.
35.
JocherGChaurasiaA.QiuJ. Yolo by ultralytics (version 8.0)[computer software]. 2023.
36.
WangC-YBochkovskiyALiaoH-YM. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Vancouver, BC, Canada, 17–24 June. 2023. IEEE.
37.
WangC-YYehI-HMark LiaoH-Y. Yolov9: Learning what you want to learn using programmable gradient information. In: European conference on computer vision, Milan, Italy, September 29–October 4 2024. Cham, Switzerland: Springer.
38.
Ministry of Land, Infrastructure and Transport. Enforcement decree of the special act on the safety control and maintenance of establishments. Sejong, Republic of Korea: Ministry of Land, Infrastructure and Transport of South Korea, 2023.
39.
Ministry of Land, Infrastructure and Transport. Detailed guidelines for safety control and maintenance of establishments (performance evaluation). South Korea: Korea Authority of Land and Infrastructures Safety, 2023.
40.
George HearnJPFriedlandIEverettT, et al. Bridge preservation and maintenance in Europe and South Africa. Report No. FHWA-PL-05-002, Washington, DC, 2005.
41.
American Association of State Highway and Transportation Officials. Transportation, manual for bridge element inspection. 2nd ed. Washington, DC: American Association of State Highway and Transportation Officials, 2019.
42.
American Association of State Highway and Transportation Officials. Transportation, manual for bridge evaluation. 3rd ed. With 2019 Interim Revisions. Washington, DC: American Association of State Highway and Transportation Officials (AASHTO), 2018.
