Exploring Deep Learning Architectures for Crack Detection: A Review of Progress and Emerging Trends

Kanchan Dhapekar; Divya Prakash

doi:10.56748/ejse.26781

Authors

Kanchan Dhapekar Poornima University https://orcid.org/0009-0006-1903-3074
Divya Prakash Poornima University

DOI:

https://doi.org/10.56748/ejse.26781

Keywords:

Deep learning, Crack detection, Object recognition, Image classification, Semantic segmentation

Abstract

Deep learning architectures, particularly those driven by computer vision and artificial intelligence advancements, have revolutionized crack detection in substructures. It is therefore crucial to categorize these papers more clearly to understand their contributions and advancements, given the increasing amount of research in this field. The comprehensive review of deep learning-based fracture recognition studies in this article highlights the significant progress made in this area. The research is grouped according to their computer vision methods and then further separated to make it easier to investigate options that use related strategies to address the crack detection issue. By examining the various architectures and approaches, this review also identifies the key experiments and restrictions faced in the field. Furthermore, it proposes critical future directions for research, drawn from the insights of the reviewed studies and emerging trends in related fields, which could help address existing gaps and drive further innovations in crack detection systems.

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References

Abbas, Q., Qureshi, I., & Ibrahim, M. E. (2021). An automatic detection and classification system of five stages for hypertensive retinopathy using semantic and instance segmentation in DenseNet architecture. Sensors, 21(20), 6936.

Aravind, N., Nagajothi, S., & Elavenil, S. (2021). Machine learning model for predicting the crack detection and pattern recognition of geopolymer concrete beams. Construction and Building Materials, 297, 123785.

Arman, M. S., Hasan, M. M., Sadia, F., Shakir, A. K., Sarker, K., & Himu, F. A. (2020). Detection and classification of road damage using R-CNN and faster R-CNN: a deep learning approach. In Cyber Security and Computer Science: Second EAI International Conference, ICONCS 2020, Dhaka, Bangladesh, February 15-16, 2020, Proceedings 2 (pp. 730-741). Springer International Publishing.

Azimi, M., Eslamlou, A. D., & Pekcan, G. (2020). Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors, 20(10), 2778.

Cha, Y. J., Ali, R., Lewis, J., & Büyükӧztürk, O. (2024). Deep learning-based structural health monitoring. Automation in Construction, 161, 105328.

Chen, H., Lin, H., & Yao, M. (2019). Improving the efficiency of encoder-decoder architecture for pixel-level crack detection. IEEE Access, 7, 186657-186670.

Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., ... & Gao, W. (2021). Pre-trained image processing transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12299-12310).

Cheng, Y., Wang, W., Ren, Z., Zhao, Y., Liao, Y., Ge, Y., ... & Zhang, C. (2023). Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 124, 103514.

Choi, Y., Bae, B., Han, T. H., & Ahn, J. (2024). Application of Mask R-CNN and YOLOv8 Algorithms for Concrete Crack Detection. IEEE Access.

Dayananda, C., Choi, J. Y., & Lee, B. (2022). A squeeze U-SegNet architecture based on residual convolution for brain MRI segmentation. IEEE Access, 10, 52804-52817.

Elhariri, E., El-Bendary, N., & Taie, S. A. (2022). Automated pixel-level deep crack segmentation on historical surfaces using U-Net models. Algorithms, 15(8), 281.

Fang, F., Li, L., Gu, Y., Zhu, H., & Lim, J. H. (2020). A novel hybrid approach for crack detection. Pattern Recognition, 107, 107474.

Feng, X., Xiao, L., Li, W., Pei, L., Sun, Z., Ma, Z., ... & Ju, H. (2020). Pavement crack detection and segmentation method based on improved deep learning fusion model. Mathematical Problems in Engineering, 2020(1), 8515213.

Golding, V. P., Gharineiat, Z., Munawar, H. S., & Ullah, F. (2022). Crack detection in concrete structures using deep learning. Sustainability, 14(13), 8117.

Gooda, S. K., Chinthamu, N., Selvan, S. T., Rajakumareswaran, V., & Brindha, G. (2023). Automatic detection of road cracks using EfficientNet with residual U-net-based segmentation and YOLOv5-based detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11, 84-91.

Guo, W., Zhong, L., Zhang, D., & Li, Q. (2024). Pavement crack detection using fractal dimension and semi-supervised learning. Fractal and Fractional, 8(8), 468.

Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 100057.

Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022). A comprehensive review of deep learning-based crack detection approaches. Applied Sciences, 12(3), 1374.

Hang, J., Wu, Y., Li, Y., Lai, T., Zhang, J., & Li, Y. (2023). A deep learning semantic segmentation network with attention mechanism for concrete crack detection. Structural Health Monitoring, 22(5), 3006-3026.

Hernández, P. G. (2023). Void characterisation of composite prepreg laminates using X-ray Imaging and Deep Learning methods (Doctoral dissertation, University of Bristol). https://gogeometry.com/software/ai/machine-learning-cognitive-mind-map.html

Kang, D. H., & Cha, Y. J. (2022). Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 21(5), 2190-2205.

Kang, D., Benipal, S. S., Gopal, D. L., & Cha, Y. J. (2020). Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction, 118, 103291.

Kar, M. K., Nath, M. K., & Neog, D. R. (2021). A review of progress in semantic image segmentation and its application to medical images. SN computer science, 2(5), 397.

Karimi, N., Mishra, M., & Lourenço, P. B. (2024). Automated surface crack detection in historical constructions with various materials using deep learning-based YOLO network. International Journal of Architectural Heritage, 1-17.

Kaveh, H., & Alhajj, R. (2024). Recent advances in crack detection technologies for structures: a survey of 2022-2023 literature. Frontiers in Built Environment, 10, 1321634.

Khanam, R., Hussain, M., Hill, R., & Allen, P. (2024). A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access.

Kulambayev, B., Astaubayeva, G., Tleuberdiyeva, G., Alimkulova, J., Nussupbekova, G., & Kisseleva, O. (2024). Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection. International Journal of Advanced Computer Science & Applications, 15(3).

Li, H., Wang, W., Wang, M., Li, L., & Vimlund, V. (2022). A review of deep learning methods for pixel-level crack detection. Journal of Traffic and Transportation Engineering (English Edition), 9(6), 945-968.

Li, H., Zong, J., Nie, J., Wu, Z., & Han, H. (2021). Pavement crack detection algorithm based on densely connected and deeply supervised network. IEEE Access, 9, 11835-11842.

Li, M., Jiang, Y., Zhang, Y., & Zhu, H. (2023). Medical image analysis using deep learning algorithms. Frontiers in Public Health, 11, 1273253.

Liu, Y., Pu, H., & Sun, D. W. (2021). Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science & Technology, 113, 193-204.

Liu, Z., Yeoh, J. K., Gu, X., Dong, Q., Chen, Y., Wu, W., ... & Wang, D. (2023). Automatic pixel-level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R-CNN. Automation in Construction, 146, 104689.

López-Monroy, A. P., & García-Salinas, J. S. (2022). Neural networks and deep learning. In Biosignal Processing and Classification Using Computational Learning and Intelligence (pp. 177-196). Academic Press.

Loverdos, D., & Sarhosis, V. (2022). Automatic image-based brick segmentation and crack detection of masonry walls using machine learning. Automation in Construction, 140, 104389.

Luo, Y., Liu, X., Song, R., Majeed, A. P. A., Zhang, F., Xu, Y., ... & Chen, W. (2024). Enhancing Bone Fracture Detection: A Feature-Based Transfer Learning Approach Using DenseNet with SVM. In 2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR).

Ma, J., Yan, W., Liu, G., Xing, S., Niu, S., & Wei, T. (2022). Complex texture contour feature extraction of cracks in timber structures of ancient architecture based on YOLO algorithm. Advances in Civil Engineering, 2022(1), 7879302

Ma, K., Meng, X., Hao, M., Huang, G., Hu, Q., & He, P. (2023). Research on the efficiency of bridge crack detection by coupling deep learning frameworks with convolutional neural networks. Sensors, 23(16), 7272.

Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906-1955.

Marin, B., Brown, K., & Erden, M. S. (2021, August). Automated masonry crack detection with faster R-CNN. In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (pp. 333-340). IEEE.

Matarneh, S., Elghaish, F., Rahimian, F. P., Abdellatef, E., & Abrishami, S. (2024). Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification. Automation in Construction, 160, 105297.

Negi, P., Kromanis, R., Dorée, A. G., & Wijnberg, K. M. (2024). Structural health monitoring of inland navigation structures and ports: a review on developments and challenges. Structural Health Monitoring, 23(1), 605-645.

Philip, R. E., Andrushia, A. D., Nammalvar, A., Gurupatham, B. G. A., & Roy, K. (2023). A comparative study on crack detection in concrete walls using transfer learning techniques. Journal of Composites Science, 7(4), 169.

Plevris, V., & Papazafeiropoulos, G. (2024). AI in Structural Health Monitoring for Infrastructure Maintenance and Safety. Infrastructures, 9(12), 225.

Qiao, W., Liu, Q., Wu, X., Ma, B., & Li, G. (2021). Automatic pixel-level pavement crack recognition using a deep feature aggregation segmentation network with a scSE attention mechanism module. Sensors, 21(9), 2902.

Rodriguez-Conde, I., Campos, C., & Fdez-Riverola, F. (2022). Optimized convolutional neural network architectures for efficient on-device vision-based object detection. Neural Computing and Applications, 34(13), 10469-10501.

Safaei, N., Smadi, O., Masoud, A., & Safaei, B. (2022). An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification. International Journal of Pavement Research and Technology, 15(1), 159-172.

Sarker, I. H. (2021). Deep learning: a comprehensive overview of techniques, taxonomy, applications, and research directions. SN computer science, 2(6), 420.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.

Sengupta, S., Basak, S., Saikia, P., Paul, S., Tsalavoutis, V., Atiah, F., ... & Peters, A. (2020). A review of deep learning with special emphasis on architecture, applications and recent trends. Knowledge-Based Systems, 194, 105596.

Shen, A., Dai, M., Hu, J., Liang, Y., Wang, S., & Du, J. (2024). Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data. arXiv preprint arXiv:2411.18622.

Singh, O., & Sengar, S. S. (2024). BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp Segmentation. arXiv preprint arXiv:2405.04288.

Sun, H., Burton, H. V., & Huang, H. (2021). Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33, 101816.

Taherdoost, H. (2024). Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems. Information, 15(8), 491.

Teng, S., Liu, Z., Chen, G., & Cheng, L. (2021). Concrete crack detection based on a well-known feature extractor model and the YOLO_v2 network. Applied Sciences, 11(2), 813.

Tibaduiza Burgos, D. A., Gomez Vargas, R. C., Pedraza, C., Agis, D., & Pozo, F. (2020). Damage identification in structural health monitoring: A brief review from its implementation to the use of data-driven applications. Sensors, 20(3), 733.

Tsalera, E., Papadakis, A., & Samarakou, M. (2021). Comparison of pre-trained CNNs for audio classification using transfer learning. Journal of Sensor and Actuator Networks, 10(4), 72.

Ullah, S. (2023). GUIDED WAVE-BASED METHODS FOR DELAMINATION IDENTIFICATION ENHANCED BY DEEP LEARNING (Doctoral dissertation, Institute of Fluid-Flow Machinery, Polish Academy of Sciences).

Van Hoang, T. (2024). Impact of integrated artificial intelligence and Internet of Things technologies on smart city transformation. Journal of Technical Education Science, 19(Special Issue 01), 64-73.

Wang, W., Shi, Y., Zhang, J., Hu, L., Li, S., He, D., & Liu, F. (2023). Traditional village building extraction based on improved Mask R-CNN: a case study of Beijing, China. Remote Sensing, 15(10), 2616.

Wu, D., Zhang, H., & Yang, Y. (2022). [Retracted] Deep Learning‐Based Crack Monitoring for Ultra‐High-Performance Concrete (UHPC). Journal of Advanced Transportation, 2022(1), 4117957.

Xu, H., Yao, L., Zhang, W., Liang, X., & Li, Z. (2019). Auto-fpn: Automatic network architecture adaptation for object detection beyond classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6649-6658).

Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., & Yang, H. (2022). Crack detection and comparison study based on faster R-CNN and mask R-CNN. Sensors, 22(3), 1215.

Yan, K., & Zhang, Z. (2021). Automated asphalt highway pavement crack detection based on deformable single shot multi-box detector under a complex environment. IEEE Access, 9, 150925-150938.

Yang, C., Chen, J., Li, Z., & Huang, Y. (2021). Structural crack detection and recognition based on deep learning. Applied sciences, 11(6), 2868.

Yang, G., Liu, K., Zhang, J., Zhao, B., Zhao, Z., Chen, X., & Chen, B. M. (2022). Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection. Construction and Building Materials, 356, 129226.

Younesi, A., Ansari, M., Fazli, M., Ejlali, A., Shafique, M., & Henkel, J. (2024). A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends. IEEE Access, 12, 41180-41218.

Yu, Z. (2022). YOLO V5s-based deep learning approach for concrete cracks detection. In SHS Web of Conferences (Vol. 144, p. 03015). EDP Sciences.

Zhang, H. (2023). Machine Vision: A Comprehensive Analysis of Techniques, Applications, and Challenges. Highlights in Science, Engineering and Technology, 71, 299-304.

Zhang, Q. (2022). Multi-sensor Data Interpretation and Fusion Frameworks for Bridge Deck Condition Assessment (Doctoral dissertation, University of Pittsburgh).

Zhang, Q., Barri, K., Babanajad, S. K., & Alavi, A. H. (2021). Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain. Engineering, 7(12), 1786-1796.

Zhang, Y., Huang, J., & Cai, F. (2020). On bridge surface crack detection based on an improved YOLO v3 algorithm. IFAC-PapersOnLine, 53(2), 8205-8210

Zhong, X., & Ban, H. (2022). Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem. Annals of Nuclear Energy, 175, 109201.