Airport Runway Crack Detection to Classify and Densify Surface Crack Type
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With the extensive development in infrastructures, many airports are built in order to satisfy travelling needs of people. The frequent arrival and departure of numerous plans lead to substantial runway damage and related safety concerns. So, the regular maintenance of runway has become an essential task specially for detection and classification of cracks in terms of owing to the intensity heterogeneity of cracks such as low real-time performance and the long time-consuming manual inspection. This paper introduces a new dataset named as ARID with 8 different crack classes. A runway crack detection model based on YOLOv5 and Faster RCNN has been proposed which is annotated on 8,228 collected datasets. Then the model is trained with different parameters for training to obtain the optimal result. Finally, based on experimental result, the crack detection precision has improved from 83% to 92%, while the recall has increased from 62.8% to 76%.
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Gopalakrishnan, Kasthurirangan. "Deep learning in data-driven pavement image analysis and automated distress detection: A review." Data 3.3 (2018): 28. https://doi.org/10.3390/data3030028
Deleted.Oliveira, Henrique, and Paulo Lobato Correia. "CrackIT—An image processing toolbox for crack detection and characterization." 2014 IEEE international conference on image processing (ICIP). IEEE, 2014. https://doi.org/10.1109/ICIP.2014.7025160
Zou, Qin, et al. "CrackTree: Automatic crack detection from pavement images." Pattern Recognition Letters 33.3 (2012): 227-238 https://doi.org/10.1016/j.patrec.2011.11.004
Zhang, Allen, et al. "Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network." Computer‐Aided Civil and Infrastructure Engineering 32.10 (2017): 805-819. https://doi.org/10.1111/mice.12297
Zalama, Eduardo, et al. "Road crack detection using visual features extracted by Gabor filters." Computer‐Aided Civil and Infrastructure Engineering 29.5 (2014): 342-358. https://doi.org/10.1111/mice.12042
Akarsu, Büşra, et al. "A fast and adaptive road defect detection approach using computer vision with real time implementation." International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (2016): 290-295. https://doi.org/10.18100/ijamec.270546
Chen, Jieh-Haur, et al. "A self organizing map optimization based image recognition and processing model for bridge crack inspection." Automation in Construction 73 (2017): 58-66. https://doi.org/10.1016/j.autcon.2016.08.033
Wang, Xianglong, and Zhaozheng Hu. "Grid-based pavement crack analysis using deep learning." 2017 4th international conference on transportation information and safety (ICTIS). IEEE, 2017. https://doi.org/10.1109/ICTIS.2017.8047878
Park, Somin, et al. "Patch-based crack detection in black box images using convolutional neural networks." Journal of Computing in Civil Engineering 33.3 (2019): 04019017. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000831
Li, Baoxian, et al. "Automatic classification of pavement crack using deep convolutional neural network." International Journal of Pavement Engineering 21.4 (2020): 457-463. https://doi.org/10.1080/10298436.2018.1485917
Dorafshan, Sattar, Robert J. Thomas, and Marc Maguire. "SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks." Data in brief 21 (2018): 1664-1668. https://doi.org/10.1016/j.dib.2018.11.015
Fan, Jiahe, et al. "Deep convolutional neural networks for road crack detection: Qualitative and quantitative comparisons." 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2021. https://doi.org/10.1109/IST50367.2021.9651375
Wang, Kelvin CP, et al. "Deep learning for asphalt pavement cracking recognition using convolutional neural network." Airfield Highw. Pavements 2017 (2017): 166-177. https://doi.org/10.1061/9780784480922.015
Zhang, Ziyu, Sanja Fidler, and Raquel Urtasun. "Instance-level segmentation for autonomous driving with deep densely connected mrfs." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. https://doi.org/10.1109/CVPR.2016.79
Pham, Quang-Hieu, et al. "Real-time progressive 3D semantic segmentation for indoor scenes." 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. https://doi.org/10.1109/WACV.2019.00121
Zhou, Zongwei, et al. "Unet++: A nested u-net architecture for medical image segmentation." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer International Publishing, 2018.
Kalfarisi, Rony, Zheng Yi Wu, and Ken Soh. "Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization." Journal of Computing in Civil Engineering 34.3 (2020): 04020010. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000890
Kang, Dongho, et al. "Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning." Automation in Construction 118 (2020): 103291.
Ni, FuTao, Jian Zhang, and ZhiQiang Chen. "Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning." Computer‐Aided Civil and Infrastructure Engineering 34.5 (2019): 367-384. https://doi.org/10.1111/mice.12421
Zhang, Kaige, H. D. Cheng, and Boyu Zhang. "Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning." Journal of Computing in Civil Engineering 32.2 (2018): 04018001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000736
Kim, Byunghyun, and Soojin Cho. "Image‐based concrete crack assessment using mask and region‐based convolutional neural network." Structural Control and Health Monitoring 26.8 (2019): e2381. https://doi.org/10.1002/stc.2381
Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. https://doi.org/10.1109/CVPR.2015.7298594
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. https://doi.org/10.1109/CVPR.2016.90
Takikawa, Towaki, et al. "Gated-scnn: Gated shape cnns for semantic segmentation." Proceedings of the IEEE/CVF international conference on computer vision. 2019. https://doi.org/10.1109/ICCV.2019.00533
Zhang, Allen, et al. "Deep learning–based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet." Journal of Computing in Civil Engineering 32.5 (2018): 04018041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000775
Fei, Yue, et al. "Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V." IEEE Transactions on Intelligent Transportation Systems 21.1 (2019): 273-284. https://doi.org/10.1109/TITS.2019.2891167
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing, 2015.
Badrinarayanan, Vijay, Ankur Handa, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling." arXiv preprint arXiv:1505.07293 (2015).
Jégou, Simon, et al. "The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017 https://doi.org/10.1109/CVPRW.2017.156
Lee, Donghan, Jeongho Kim, and Daewoo Lee. "Robust concrete crack detection using deep learning-based semantic segmentation." International Journal of Aeronautical and Space Sciences 20 (2019): 287-299 https://doi.org/10.1007/s42405-018-0120-5
Yokoyama, Suguru, and Takashi Matsumoto. "Development of an automatic detector of cracks in concrete using machine learning." Procedia engineering 171 (2017): 1250-1255. https://doi.org/10.1016/j.proeng.2017.01.418
Cha, Young‐Jin, Wooram Choi, and Oral Büyüköztürk. "Deep learning‐based crack damage detection using convolutional neural networks." Computer‐Aided Civil and Infrastructure Engineering 32.5 (2017): 361-378. https://doi.org/10.1111/mice.12263
Pauly, Leo, et al. "Deeper networks for pavement crack detection." Proceedings of the 34th ISARC. IAARC, 2017. https://doi.org/10.22260/ISARC2017/0066
Feng, Chen, et al. "Deep active learning for civil infrastructure defect detection and classification." Computing in civil engineering 2017. 2017. 298-306. https://doi.org/10.1061/9780784480823.036
Eisenbach, Markus, et al. "How to get pavement distress detection ready for deep learning? A systematic approach." 2017 international joint conference on neural networks (IJCNN). IEEE, 2017. https://doi.org/10.1109/IJCNN.2017.7966101
Dorafshan, Sattar, et al. "Deep learning neural networks for sUAS-assisted structural inspections: Feasibility and application." 2018 international conference on unmanned aircraft systems (ICUAS). IEEE, 2018. https://doi.org/10.1109/ICUAS.2018.8453409
Silva, Wilson Ricardo Leal da, and Diogo Schwerz de Lucena. "Concrete cracks detection based on deep learning image classification." Proceedings. Vol. 2. No. 8. MDPI, 2018. https://doi.org/10.3390/ICEM18-05387
Kim, Hyunjun, et al. "Crack and noncrack classification from concrete surface images using machine learning." Structural Health Monitoring 18.3 (2019): 725-738. https://doi.org/10.1177/1475921718768747
Kim, Byunghyun, and Soojin Cho. "Automated vision-based detection of cracks on concrete surfaces using a deep learning technique." Sensors 18.10 (2018): 3452. https://doi.org/10.3390/s18103452
Zhang, Kaige, Heng-Da Cheng, and Shan Gai. "Efficient dense-dilation network for pavement cracks detection with large input image size." 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. https://doi.org/10.1109/ITSC.2018.8569958
Tan, Chenjun, Nasim Uddin, and Yahya M. Mohammed. "Deep learning-based crack detection using mask R-CNN technique." 9 th International Conference on Structural Health Monitoring of Intelligent Infrastructure. 2019. https://doi.org/10.1109/ICIP.2019.8803357
Fang, Fen, et al. "Towards real-time crack detection using a deep neural network with a Bayesian fusion algorithm." 2019 IEEE international conference on image processing (ICIP). IEEE, 2019. https://doi.org/10.1109/CVPR.2016.90
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Jenkins, Mark David, et al. "A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks." 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018.
Fan, Zhun, et al. "Automatic pavement crack detection based on structured prediction with the convolutional neural network." arXiv preprint arXiv:1802.02208 (2018).
Zhang, Allen, et al. "Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network." Computer‐Aided Civil and Infrastructure Engineering 32.10 (2017): 805-819. https://doi.org/10.1111/mice.12297
Li, Shengyuan, Xuefeng Zhao, and Guangyi Zhou. "Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network." Computer‐Aided Civil and Infrastructure Engineering 34.7 (2019): 616-634. https://doi.org/10.1111/mice.12433
Bang, Seongdeok, et al. "Encoder–decoder network for pixel‐level road crack detection in black‐box images." Computer‐Aided Civil and Infrastructure Engineering 34.8 (2019): 713-727. https://doi.org/10.1111/mice.12440
Zhang, Kaige, Yingtao Zhang, and Heng-Da Cheng. "CrackGAN: Pavement crack detection using partially accurate ground truths based on generative adversarial learning." IEEE Transactions on Intelligent Transportation Systems 22.2 (2020): 1306-1319. https://doi.org/10.1109/TITS.2020.2990703
Mei, Qipei, and Mustafa Gül. "Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones." Structural Health Monitoring 19.6 (2020): 1726-1744. https://doi.org/10.1177/1475921719896813
Chen, Tingyang, et al. "Pavement crack detection and recognition using the architecture of segNet." Journal of Industrial Information Integration 18 (2020): 100144. https://doi.org/10.1016/j.jii.2020.100144
Zou, Qin, et al. "Deepcrack: Learning hierarchical convolutional features for crack detection." IEEE Transactions on Image Processing 28.3 (2018): 1498-1512. https://doi.org/10.1109/TIP.2018.2878966
Yang, Fan, et al. "Feature pyramid and hierarchical boosting network for pavement crack detection." IEEE Transactions on Intelligent Transportation Systems 21.4 (2019): 1525-1535. https://doi.org/10.1109/TITS.2019.2910595
Mei, Qipei, and Mustafa Gül. "A cost effective solution for pavement crack inspection using cameras and deep neural networks." Construction and Building Materials 256 (2020): 119397. https://doi.org/10.1016/j.conbuildmat.2020.119397
Thuan, Do. "Evolution of Yolo algorithm and Yolov5: The State-of-the-Art object detention algorithm." (2021).
Qiu, Qiwen, and Denvid Lau. "Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images." Automation in Construction 147 (2023): 104745. https://doi.org/10.1016/j.autcon.2023.104745
Deng, Jianghua, Ye Lu, and Vincent CS Lee. "A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference." Measurement 216 (2023): 112892. https://doi.org/10.1016/j.measurement.2023.112892
Chen, Zhuo, Xiaoke Huang, and Siqi Liu. "Pavement crack identification and detection based on multi-task learning." 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. https://doi.org/10.1109/EEBDA56825.2023.10090657
Kim, Bubryur, et al. "Surface crack detection using deep learning with shallow CNN architecture for enhanced computation." Neural Computing and Applications 33 (2021): 9289-9305. https://doi.org/10.1007/s00521-021-05690-8
Tang, Youzhi, et al. "Pixel-level pavement crack segmentation with encoder-decoder network." Measurement 184 (2021): 10 https://doi.org/10.1016/j.measurement.2021.109914