Research Advance of Crack Detection for Infrastructure Surfaces Based on Deep Learning

doi:10.3778/j.issn.1002-8331.2407-0407

Abstract

Abstract: Civil infrastructure is prone to changes in physical or performance after long-term use, and causing certain damage to the function and service safety. So it is essential to monitor structure healthy of such facilities. Crack detection is an extremely important part of structure healthy monitoring. Timely detection and identification of such damage can effectively avoid severe accidents. Crack detection methods based on computer vision are simple, fast and accurate, and are widely used for surface crack detection in civil infrastructures. This paper reviews crack detection methods for infrastructure surfaces based on deep learning from three different detection directions: image classification, object detection, and semantic segmentation. And common data collection methods and commonly used public crack datasets are summarized. Finally, the difficulties and challenges of deep learning-based surface crack detection methods for infrastructures are discussed, and possible future development directions are envisioned.

Key words: structure health monitoring, crack detection, computer vision, deep leaning

摘要： 民用基础设施在长期使用后容易发生物理结构或性能状态的改变，对其功能和使用安全造成一定的损害，因此，对这类设施的结构健康监测是十分必要的。裂纹检测是结构健康监测中极其重要的一部分，及时检测并识别这类损伤，能有效避免事故的发生。基于计算机视觉的表面裂纹检测方法操作简单、检测速度快、准确率高，被广泛应用于民用基础设施的表面裂纹检测。从图像分类、目标检测、语义分割三个不同的检测方向综述了基于深度学习的基础设施表面裂纹检测方法，总结了常见的数据采集方法和常用的公共裂纹数据集。最后讨论了基于深度学习的基础设施表面裂纹检测方法存在的困难与挑战，并展望了未来可能的发展方向。

关键词: 结构健康监测, 裂纹检测, 计算机视觉, 深度学习

HU Xiangkun, LI Hua, FENG Yixiong, QIAN Songrong, LI Jian, LI Shaobo. Research Advance of Crack Detection for Infrastructure Surfaces Based on Deep Learning[J]. Computer Engineering and Applications, 2025, 61(1): 1-23.

胡翔坤, 李华, 冯毅雄, 钱松荣, 李键, 李少波. 基于深度学习的基础设施表面裂纹检测方法研究进展[J]. 计算机工程与应用, 2025, 61(1): 1-23.

References

[1] DONG C Z, CATBAS F N. A review of computer vision-based structural health monitoring at local and global levels[J]. Structural Health Monitoring, 2021, 20(2): 692-743.
[2] ABDEL-QADER I, ABUDAYYEH O, KELLY M E. Analysis of edge-detection techniques for crack identification in bridges[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 255-263.
[3] WU L J, LIN X, CHEN Z C, et al. Surface crack detection based on image stitching and transfer learning with pretrained convolutional neural network[J]. Structural Control and Health Monitoring, 2021, 28(8): e2766.
[4] MAZARIO E, VENEGAS R, HERRASTI P, et al. Pitting corrosion and stress corrosion cracking study in high strength steels in alkaline media[J]. Journal of Solid State Electrochemistry, 2016, 20: 1223-1227.
[5] HSIEH Y A, TSAI Y J. Machine learning for crack detection: review and model performance comparison[J]. Journal of Computing in Civil Engineering, 2020, 34(5): 04020038.
[6] OLIVEIRA H, CORREIA P L. Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 14(1): 155-168.
[7] GAO X D, CHEN Y Q, YOU D Y, et al. Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network[J]. Mechanical Systems and Signal Processing, 2017, 84: 570-583.
[8] LIU H, XIA H Y, ZHUANG M W, et al. Reverse time migration of acoustic waves for imaging based defects detection for concrete and CFST structures[J]. Mechanical Systems and Signal Processing, 2019, 117: 210-220.
[9] GREENHALL J, GRUTZIK S, GRAHAM A, et al. Nonlinear acoustic crack detection in thermoelectric wafers[J]. Mechanical Systems and Signal Processing, 2020, 139: 106598.
[10] YANG R L, WANG S, WU X, et al. Using lightweight convolutional neural network to track vibration displacement in rotating body video[J]. Mechanical Systems and Signal Processing, 2022, 177: 109137.
[11] HAMISHEBAHAR Y, GUAN H, SO S, et al. A comprehensive review of deep learning-based crack detection approaches[J]. Applied Sciences, 2022, 12(3): 1374.
[12] WANG S, LIU C, ZHANG Y H. Fully convolution network architecture for steel-beam crack detection in fast-stitching images[J]. Mechanical Systems and Signal Processing, 2022, 165: 108377.
[13] ZHANG Y, YUEN K V. Crack detection using fusion features‐based broad learning system and image processing[J]. Computer‐Aided Civil and Infrastructure Engineering, 2021, 36(12): 1568-1584.
[14] PRASANNA P, DANA K J, GUCUNSKI N, et al. Automated crack detection on concrete bridges[J]. IEEE Transactions on Automation Science and Engineering, 2014, 13(2): 591-599.
[15] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[16] LE Q V. Building high-level features using large scale unsupervised learning[C]//Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013: 8595-8598.
[17] CHAMBON S, MOLIARD J M. Automatic road pavement assessment with image processing: review and comparison[J]. International Journal of Geophysics, 2011: 179.
[18] KOCH C, GEORGIEVA K, KASIREDDY V, et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure[J]. Advanced Engineering Informatics, 2015, 29(2): 196-210.
[19] LI S Y, ZHAO X F, ZHOU G Y. Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network[J]. Computer?Aided Civil and Infrastructure Engineering, 2019, 34(7): 616-634.
[20] DAI B, GU C S, ZHAO E F, et al. Improved online sequential extreme learning machine for identifying crack behavior in concrete dam[J]. Advances in Structural Engineering, 2019, 22(2): 402-412.
[21] FENG C C, ZHANG H, WANG H R, et al. Automatic pixel-level crack detection on dam surface using deep convolutional network[J]. Sensors, 2020, 20(7): 2069.
[22] ZAKERI H, NEJAD F M, FAHIMIFAR A. Image based techniques for crack detection, classification and quantification in asphalt pavement: a review[J]. Archives of Computational Methods in Engineering, 2017, 24: 935-977.
[23] JAHANSHAHI M R, KELLY J S, MASRI S F, et al. A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures[J]. Structure and Infrastructure Engineering, 2009, 5(6): 455-486.
[24] TABERNIK D, ?ELA S, SKVAR? J, et al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of Intelligent Manufacturing, 2020, 31(3): 759-776.
[25] ZHANG A, WANG K C P, JI R, et al. Efficient system of cracking-detection algorithms with 1-mm 3D-surface models and performance measures[J]. Journal of Computing in Civil Engineering, 2016, 30(6): 04016020.
[26] MOHAN A, POOBAL S. Crack detection using image processing: a critical review and analysis[J]. Alexandria Engineering Journal, 2018, 57(2): 787-798.
[27] MUNAWAR H S, HAMMAD A W A, HADDAD A, et al. Image-based crack detection methods: a review[J]. Infrastructures, 2021, 6(8): 115.
[28] ALI L, ALNAJJAR F, KHAN W, et al. Bibliometric analysis and review of deep learning-based crack detection literature published between 2010 and 2022[J]. Buildings, 2022, 12(4): 432.
[29] GOPALAKRISHNAN K, KHAITAN S K, CHOUDHARY A, et al. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection[J]. Construction and Building Materials, 2017, 157: 322-330.
[30] HOU Y, LI Q H, ZHANG C, et al. The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis[J]. Engineering, 2021, 7(6): 845-856.
[31] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409. 1556, 2014.
[32] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[33] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
[34] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[35] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv:2010.11929, 2020.
[36] BAO Y Q, LI H. Machine learning paradigm for structural health monitoring[J]. Structural Health Monitoring, 2021, 20(4): 1353-1372.
[37] AZIMI M, ESLAMLOU A D, PEKCAN G. Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review[J]. Sensors, 2020, 20(10): 2778.
[38] GUO F, QIAN Y, LIU J, et al. Pavement crack detection based on transformer network[J]. Automation in Construction, 2023, 145: 104646.
[39] YANG Y S, YANG C M, HUANG C W. Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis[J]. Advances in Engineering Software, 2015, 83: 99-108.
[40] YANG Y, NAGARAJAIAH S. Dynamic imaging: real-time detection of local structural damage with blind separation of low-rank background and sparse innovation[J]. Journal of Structural Engineering, 2016, 142(2): 04015144.
[41] LI H F, SONG D Z, LIU Y, et al. Automatic pavement crack detection by multi-scale image fusion[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2025-2036.
[42] MCLAUGHLIN E, CHARRON N, NARASIMHAN S. Automated defect quantification in concrete bridges using robotics and deep learning[J]. Journal of Computing in Civil Engineering, 2020, 34(5): 04020029.
[43] LIM R S, LA H M, SHENG W. A robotic crack inspection and mapping system for bridge deck maintenance[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 367-378.
[44] LUO S, YAO J Y, HU J, et al. Using deep learning-based defect detection and 3D quantitative assessment for steel deck pavement maintenance[J]. IEEE Transactions on Intelligent Transportation Systems, 2022: 1-10.
[45] ZHANG A, WANG K C P, LI B, et al. Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network[J]. Computer‐Aided Civil and Infrastructure Engineering, 2017, 32(10): 805-819.
[46] CHEN S E, RICE C, BOYLE C, et al. Small-format aerial photography for highway-bridge monitoring[J]. Journal of Performance of Constructed Facilities, 2011, 25(2): 105-112.
[47] YAN Y J, MAO Z, WU J H, et al. Towards automated detection and quantification of concrete cracks using integrated images and lidar data from unmanned aerial vehicles[J]. Structural Control and Health Monitoring, 2021, 28(8): e2757.
[48] ZOU Q, CAO Y, LI Q Q, et al. CrackTree: automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3): 227-238.
[49] AMHAZ R, CHAMBON S, IDIER J, et al. Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2718-2729.
[50] SHI Y, CUI L M, QI Z Q, et al. Automatic road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(12): 3434-3445.
[51] EISENBACH M, STRICKER R, SEICHTER D, et al. How to get pavement distress detection ready for deep learning? A systematic approach[C]//Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), 2017: 2039-2047.
[52] ZOU Q, ZHANG Z, LI Q Q, et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 2018, 28(3): 1498-1512.
[53] DORAFSHAN S, THOMAS R J, MAGUIRE M. SDNET2018: an annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks[J]. Data in Brief, 2018, 21: 1664-1668.
[54] YANG F, ZHANG L, YU S J, et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(4): 1525-1535.
[55] LIU Y H, YAO J, LU X H, et al. DeepCrack: a deep hierarchical feature learning architecture for crack segmentation[J]. Neurocomputing, 2019, 338: 139-153.
[56] XU H Y, SU X, WANG Y, et al. Automatic bridge crack detection using a convolutional neural network[J]. Applied Sciences, 2019, 9(14): 2867.
[57] ZHOU S L, SONG W. Deep learning-based roadway crack classification using laser-scanned range images: a comparative study on hyperparameter selection[J]. Automation in Construction, 2020, 114: 103171.
[58] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
[59] KHERADMANDI N, MEHRANFAR V. A critical review and comparative study on image segmentation-based techniques for pavement crack detection[J]. Construction and Building Materials, 2022, 321: 126162.
[60] QUAN Y W, SUN J, ZHANG Y, et al. The method of the road surface crack detection by the improved Otsu threshold[C]//Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2019: 1615-1620.
[61] YU L, TIAN Y G, WU W. A dark target detection method based on the adjacency effect: a case study on crack detection[J]. Sensors, 2019, 19(12): 2829.
[62] LI P, WANG C, LI S M, et al. Research on crack detection method of airport runway based on twice-threshold segmentation[C]//Proceedings of the 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015: 1716-1720.
[63] AL-AMRI S S, KALYANKAR N V, KHAMITKAR S D. Image segmentation by using edge detection[J]. International Journal on Computer Science and Engineering, 2010, 2(3): 804-807.
[64] ZHU Q B. Pavement crack detection algorithm based on image processing analysis[C]//Proceedings of the 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016: 15-18.
[65] AYENU-PRAH A, ATTOH-OKINE N. Evaluating pavement cracks with bidimensional empirical mode decomposition[J]. EURASIP Journal on Advances in Signal Processing, 2008: 1-7.
[66] SONG Q, LIN G Y, MA J Q, et al. An edge-detection method based on adaptive canny algorithm and iterative segmentation threshold[C]//Proceedings of the 2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE), 2016: 64-67.
[67] OUMA Y O, HAHN M. Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform[J]. Advanced Engineering Informatics, 2016, 30(3): 481-499.
[68] DORAFSHAN S, THOMAS R J, MAGUIRE M. Benchmarking image processing algorithms for unmanned aerial system-assisted crack detection in concrete structures[J]. Infrastructures, 2019, 4(2): 19.
[69] LI Q Q, ZOU Q, ZHANG D Q, et al. FoSA: F* seed-growing approach for crack-line detection from pavement images[J]. Image and Vision Computing, 2011, 29(12): 861-872.
[70] SONG M P, CUI D Q, YU C Y, et al. Crack detection algorithm for photovoltaic image based on multi-scale pyramid and improved region growing[C]//Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018: 128-132.
[71] SARI Y, PRAKOSO P B, BASKARA A R. Road crack detection using support vector machine (SVM) and OTSU algorithm[C]//Proceedings of the 2019 6th International Conference on Electric Vehicular Technology (ICEVT), 2019: 349-354.
[72] CHEN C, SEO H, JUN C H, et al. Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM[J]. International Journal of Pavement Engineering, 2022, 23(9): 3274-3283.
[73] SHENG P, CHEN L, TIAN J. Learning-based road crack detection using gradient boost decision tree[C]//Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018: 1228-1232.
[74] ZHANG W Y, ZHANG Z J, QI D P, et al. Automatic crack detection and classification method for subway tunnel safety monitoring[J]. Sensors, 2014, 14(10): 19307-19328.
[75] ZALAMA E, GóMEZ‐GARCíA‐BERMEJO J, MEDINA R, et al. Road crack detection using visual features extracted by Gabor filters[J]. Computer‐Aided Civil and Infrastructure Engineering, 2014, 29(5): 342-358.
[76] WANG S B, YANG F, CHENG Y M, et al. Adaboost-based crack detection method for pavement[J]. IOP Conference Series: Earth and Environmental Science, 2018, 189(2): 022005.
[77] NOH Y, KOO D, KANG Y M, et al. Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering[C]//Proceedings of the 2017 International Conference on Applied System Innovation (ICASI), 2017: 877-880.
[78] FAN X N, WU J J, SHI P F, et al. A novel automatic dam crack detection algorithm based on local-global clustering[J]. Multimedia Tools and Applications, 2018, 77: 26581-26599.
[79] DORAFSHAN S, THOMAS R J, MAGUIRE M. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete[J]. Construction and Building Materials, 2018, 186: 1031-1045.
[80] FEI Y, WANG K C P, ZHANG A, et al. Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(1): 273-284.
[81] TAO X, ZHANG D P, MA W Z, et al. Automatic metallic surface defect detection and recognition with convolutional neural networks[J]. Applied Sciences, 2018, 8(9): 1575.
[82] CHENG Y, CHEN J J, LI Z Y, et al. Structural crack detection and recognition based on deep learning[J]. Applied Sciences, 2021, 11(6): 2868.
[83] MITTEL D, KERBER F. Vision-based crack detection using transfer learning in metal forming processes[C]//Proceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2019: 544-551.
[84] QU Z, MEI J, LIU L, et al. Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model[J]. IEEE Access, 2020, 8: 54564-54573.
[85] WANG Z W, FENG J, ZHANG T. Asphalt pavement crack image screening by transformer-based model[C]//Proceedings of the 2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), 2022: 129-133.
[86] CHAIYASARN K, SHARMA M, ALI L, et al. Crack detection in historical structures based on convolutional neural network[J]. GEOMATE Journal, 2018, 15(51): 240-251.
[87] ZHANG X Y, WANG X D. An effective bridge cracks classification method based on machine learning[C]//Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, 2020: 790-794.
[88] FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 3146-3154.
[89] SHANG J, XU J, ZHANG A A, et al. Automatic pixel-level pavement sealed crack detection using multi-fusion U-Net network[J]. Measurement, 2023, 208: 112475.
[90] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
[91] CHA Y J, CHOI W, BüYüK?ZTüRK O. Deep learning‐based crack damage detection using convolutional neural networks[J]. Computer‐Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378.
[92] LIU J W, YANG X, LAU S, et al. Automated pavement crack detection and segmentation based on two‐step convolutional neural network[J]. Computer‐Aided Civil and Infrastructure Engineering, 2020, 35(11): 1291-1305.
[93] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, Oct 11-14, 2016. Cham: Springer International Publishing, 2016: 21-37.
[94] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[95] KIM I H, JEON H, BAEK S C, et al. Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle[J]. Sensors, 2018, 18(6): 1881.
[96] GOU C, PENG B, LI T R, et al. Pavement crack detection based on the improved faster-RCNN[C]//Proceedings of the 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019: 962-967.
[97] LI R X, YU J Y, LI F, et al. Automatic bridge crack detection using unmanned aerial vehicle and Faster R-CNN[J]. Construction and Building Materials, 2023, 362: 129659.
[98] SEKAR A, PERUMAL V. Automatic road crack detection and classification using multi-tasking faster RCNN[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41(6): 6615-6628.
[99] ZHU W, ZHANG H, EASTWOOD J, et al. Concrete crack detection using lightweight attention feature fusion single shot multibox detector[J]. Knowledge-Based Systems, 2023, 261: 110216.
[100] NIE M, WANG C. Pavement crack detection based on yolo v3[C]//Proceedings of the 2019 2nd International Conference on Safety Produce Informatization (IICSPI), 2019: 327-330.
[101] DU Y C, PAN N, XU Z H, et al. Pavement distress detection and classification based on YOLO network[J]. International Journal of Pavement Engineering, 2021, 22(13): 1659-1672.
[102] INAM H, ISLAM N U, AKRAM M U, et al. Smart and automated infrastructure management: a deep learning approach for crack detection in bridge images[J]. Sustainability, 2023, 15(3): 1866.
[103] YU G, ZHOU X L. An improved YOLOv5 crack detection method combined with a bottleneck transformer[J]. Mathematics, 2023, 11(10): 2377.
[104] DUAN S Q, ZHANG M H, QIU S L, et al. Tunnel lining crack detection model based on improved YOLOv5[J]. Tunnelling and Underground Space Technology, 2024, 147: 105713.
[105] SU P, HAN H Z, LIU M, et al. MOD-YOLO: rethinking the YOLO architecture at the level of feature information and applying it to crack detection[J]. Expert Systems with Applications, 2024, 237: 121346.
[106] LUO H, LI J M, CAI L M, et al. STrans-YOLOX: fusing swin transformer and YOLOX for automatic pavement crack detection[J]. Applied Sciences, 2023, 13(3): 1999.
[107] WAN H F, GAO L, YUAN Z D, et al. A novel transformer model for surface damage detection and cognition of concrete bridges[J]. Expert Systems with Applications, 2023, 213: 119019.
[108] QINGYI W, BO C. A novel transfer learning model for the real-time concrete crack detection[J]. Knowledge-Based Systems, 2024: 112313.
[109] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[110] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015: 91-99.
[111] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer International Publishing, 2020: 213-229.
[112] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[113] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, Oct 5-9, 2015. Cham: Springer International Publishing, 2015: 234-241.
[114] DUNG C V, ANH L D. Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction, 2019, 99: 52-58.
[115] CHAIYASARN K, BUATIK A, MOHAMAD H, et al. Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures[J]. Automation in Construction, 2022, 140: 104388.
[116] ALFAZ N, HASNAT A, KHAN A M R N, et al. Bridge crack detection using dense convolutional network (DenseNet)[C]//Proceedings of the 2nd International Conference on Computing Advancements, 2022: 509-515.
[117] ZHENG X, ZHANG S L, LI X, et al. Lightweight bridge crack detection method based on SegNet and bottleneck depth-separable convolution with residuals[J]. IEEE Access, 2021, 9: 161649-161668.
[118] LIU Z Q, CAO Y W, WANG Y Z, et al. Computer vision-based concrete crack detection using U-Net fully convolutional networks[J]. Automation in Construction, 2019, 104: 129-139.
[119] BHOWMICK S, NAGARAJAIAH S, VEERARAGHAVAN A. Vision and deep learning-based algorithms to detect and quantify cracks on concrete surfaces from UAV videos[J]. Sensors, 2020, 20(21): 6299.
[120] LI P G, XIA H T, ZHOU B, et al. A method to improve the accuracy of pavement crack identification by combining a semantic segmentation and edge detection model[J]. Applied Sciences, 2022, 12(9): 4714.
[121] ZHU G J, LIU J C, FAN Z, et al. A lightweight encoder-decoder network for automatic pavement crack detection[J]. Computer‐Aided Civil and Infrastructure Engineering, 2024, 39(12): 1743-1765.
[122] LIU H J, MIAO X Y, MERTZ C, et al. Crackformer: Transformer network for fine-grained crack detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 3783-3792.
[123] LIU H J, YANG J, MIAO X Y, et al. CrackFormer network for pavement crack segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 9240-9252.
[124] SHAMSABADI E A, XU C, RAO A S, et al. Vision transformer-based autonomous crack detection on asphalt and concrete surfaces[J]. Automation in Construction, 2022, 140: 104316.
[125] XU Z S, GUAN H Y, KANG J, et al. Pavement crack detection from CCD images with a locally enhanced transformer network[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 110: 102825.
[126] ZHANG E H, SHAO L H, WANG Y. Unifying transformer and convolution for dam crack detection[J]. Automation in Construction, 2023, 147: 104712.
[127] GUO J M, MARKONI H. Efficient and adaptable patch-based crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 21885-21896.
[128] WANG Z L, LENG Z F, ZHANG Z X. A weakly-supervised transformer-based hybrid network with multi-attention for pavement crack detection[J]. Construction and Building Materials, 2024, 411: 134134.
[129] JéGOU S, DROZDZAL M, VAZQUEZ D, et al. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017: 11-19.
[130] HUANG G, LIU Z, MAATEN L V D, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
[131] GUO J M, MARKONI H. Transformer based refinement network for accurate crack detection[C]//Proceedings of the 2021 International Conference on System Science and Engineering (ICSSE), 2021: 442-446.
[132] SHAMSABADI E A, XU C, DIAS-DA-COSTA D. Robust crack detection in masonry structures with Transformers[J]. Measurement, 2022, 200: 111590.
[133] WANG S, LI B Z, KHABSA M, et al. Linformer: self-attention with linear complexity[J]. arXiv:2006.04768, 2020.
[134] LIN C M, TIAN D X, DUAN X T, et al. TransCrack: revisiting fine-grained road crack detection with a transformer design[J]. Philosophical Transactions of the Royal Society A, 2023, 381(2254): 20220172.
[135] LI G, WAN J, HE S H, et al. Semi-supervised semantic segmentation using adversarial learning for pavement crack detection[J]. IEEE Access, 2020, 8: 51446-51459.
[136] WANG W J, SU C. Semi-supervised semantic segmentation network for surface crack detection[J]. Automation in Construction, 2021, 128: 103786.
[137] INOUE Y, NAGAYOSHI H. Weakly-supervised crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 12050-12061.
[138] DONG Z M, WANG J J, CUI B, et al. Patch-based weakly supervised semantic segmentation network for crack detection[J]. Construction and Building Materials, 2020, 258: 120291.
[139] OKSUZ K, CAM B C, AKBAS E, et al. A ranking-based, balanced loss function unifying classification and localisation in object detection[C]//Advances in Neural Information Processing Systems, 2020, 33: 15534-15545.
[140] ZHANG K G, ZHANG Y T, CHENG H D. CrackGAN: pavement crack detection using partially accurate ground truths based on generative adversarial learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(2): 1306-1319.
[141] KIM J, SHIM S, CHA Y, et al. Lightweight pixel-wise segmentation for efficient concrete crack detection using hierarchical convolutional neural network[J]. Smart Materials and Structures, 2021, 30(4): 045023.
[142] HOU Y, SHI H Y, CHEN N, et al. Vision image monitoring on transportation infrastructures: a lightweight transfer learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 12888-12899.
[143] CHEN C L P, LIU Z L. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29(1): 10-24.
[144] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[J]. arXiv:1503.02531, 2015.
[145] YU R C, LI A, CHEN C F, et al. NISP: pruning networks using neuron importance score propagation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9194-9203.