Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Lili Fan; Shen Li; Ying Li; Bai Li; Dongpu Cao; Fei-Yue Wang

doi:10.1109/JAS.2023.123447

Volume 10 Issue 7

Jul. 2023

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(7): 1593-1607

L. L. Fan, S. Li, Y. Li, B. Li, D. P. Cao, and F.-Y. Wang, “Pavement cracks coupled with shadows: A new shadow-crack dataset and a shadow-removal-oriented crack detection approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1593–1607, Jul. 2023. doi: 10.1109/JAS.2023.123447

Citation:

L. L. Fan, S. Li, Y. Li, B. Li, D. P. Cao, and F.-Y. Wang, “Pavement cracks coupled with shadows: A new shadow-crack dataset and a shadow-removal-oriented crack detection approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1593–1607, Jul. 2023. doi: 10.1109/JAS.2023.123447

Citation:

L. L. Fan, S. Li, Y. Li, B. Li, D. P. Cao, and F.-Y. Wang, “Pavement cracks coupled with shadows: A new shadow-crack dataset and a shadow-removal-oriented crack detection approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1593–1607, Jul. 2023. doi: 10.1109/JAS.2023.123447

PDF( 13026 KB)

Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

doi: 10.1109/JAS.2023.123447

Funds: This work was supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China (2021YFD2000304), Fundamental Research Funds for the Central Universities (531118010509), and Natural Science Foundation of Hunan Province, China (2021JJ40114)

More Information

Author Bio:
Lili Fan received the Ph.D. degree in computer application technology from Jilin University in 2021. She is currently a Postdoctoral Researcher at the School of Information and Electronics, Beijing Institute of Technology. Before this, she worked in Tsinghua University and University of Waterloo, Canada from 2020−2022 as a Visiting Scholar. She was a recipient of “Best Application Prize” in the 2nd International Conference on Digital Twins and Parallel Intelligence. Her research interests include computer vision tasks based on brain-inspired intelligence, including but not limited to various deep learning based tasks, such as image retrieval, semantic segmentation, and object detection

Shen Li received the Ph.D. degree at the University of Wisconsin-Madison, USA, in 2018. He is a Research Associate at Tsinghua University. His research interests include intelligent transportation systems, architecture design of CAVH system, vehicle-infrastructure cooperative planning and decision method, traffic data mining based on cellular data, and traffic operations and management

Ying Li received the Ph.D. degree with the Mobile Sensing and Geodata Science Laboratory, Department of Geography and Environmental Management, University of Waterloo, Canada. She is now an Assistant Professor at the School of Mechanical Engineering, Beijing Institute of Technology. Her research interests include autonomous driving, mobile laser scanning, intelligent processing of point clouds, geometric and semantic modeling, and augmented reality

Bai Li (Member, IEEE) received the B.S. degree in 2013 from Beihang University and the Ph.D. degree from Zhejiang University. From November 2016 to June 2017, he visited the Department of Civil and Environmental Engineering, University of Michigan (Ann Arbor), USA, as a joint training Ph.D. student. He is currently an Associate Professor at the College of Mechanical and Vehicle Engineering, Hunan University. Prior to joining Hunan University, he worked in JDX R&D Center of Automated Driving, JD Inc. from 2018 to 2020 as an Algorithm Engineer. He has been the first author of nearly 70 journal/conference papers and 2 books in robotics. He was a recipient of the International Federation of Automatic Control (IFAC) 2014–2016 Best Journal Paper Prize. Since 2022, he has served as an Associate Editor for IEEE Transactions on Intelligent Vehicles (TIV). He won the 2022 Best Associate Editor Award of TIV. His research interests include perception, planning, and tracking control of automated vehicles

Dongpu Cao (Member, IEEE) received the Ph.D. degree from Concordia University, Canada, in 2008. He is a Professor at Tsinghua University. His current research interests include driver cognition, automated driving and cognitive autonomous driving. He has contributed more than 200 papers and 3 books. He received the SAE Arch T. Colwell Merit Award in 2012, IEEE VTS 2020 Best Vehicular Electronics Paper Award and 6 Best Paper Awards from international conferences. Prof. Cao has served as Deputy Editor-in-Chief for IET Intelligent Transport Systems Journal and Associate Editors for IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Computational Social Systems, and ASME Journal of Dynamic Systems, Measurement, and Control. Prof. Cao is an IEEE VTS Distinguished Lecturer

Fei-Yue Wang (Fellow, IEEE) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, USA, in 1990. In 1990, he joined the University of Arizona, USA, and became a Professor and the Director of Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center, Institute of Automation, Chinese Academy of Sciences (CAS), under the support of Outstanding Chinese Talents Program from State Planning Council, and in 2002, he was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS, and the Vice President of the Institute of Automation, CAS, in 2006. In 2011, he became the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems. He has been the Chief Judge of Intelligent Vehicles Future Challenge since 2009 and the Director of China Intelligent Vehicles Proving Center with Changshu, since 2015. He is currently the Director of Intel’s International Collaborative Research Institute on Parallel Driving with CAS and Tsinghua University. His research interests include methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he was the recipient of the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. He was the recipient of IEEE Intelligent Transportation Systems Society outstanding Application and Research awards in 2009, 2011, and 2015, respectively, IEEE Systems, Man, and Cybernetics Society Norbert Wiener Award in 2014, and became the IFAC Pavel J. Nowacki Distinguished Lecturer in 2021. Since 1997, he has been the General or Program Chair of more than 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of IEEE Intelligent Transportation Systems Society from 2005 to 2007, IEEE Council of RFID from 2019 to 2021, Chinese Association for Science and Technology, USA, in 2005, American Zhu Kezhen Education Foundation from 2007 to 2008, Vice President of the ACM China Council from 2010 to 2011, Vice President and Secretary General of the Chinese Association of Automation from 2008 to 2018, and Vice President of IEEE Systems, Man, and Cybernetics Society from 2019 to 2021. He was the Founding Editor-in-Chief (EIC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, IEEE Intelligence Systems Magazine from 2006 to 2007, IEEE/CAA Journal of Automatica Sinica from 2014 to 2017, China’s Journal of Command and Control from 2015 to 2021, and Chinese Journal of Intelligent Science and Technology from 2019 to 2021. He was the EIC of IEEE Intelligence Systems from 2009 to 2012, IEEE Transactions on Intelligence Transportation Systems from 2009 to 2016, and IEEE Transactions on Computational Social Systems from 2017 to 2020. He is also the President of CAA’s Supervision Council, and the present EIC of IEEE Transportation on Intelligence Vehicles
Corresponding author: Shen Li, e-mail: sli299@tsinghua.edu.cn
Received Date: 2022-12-08
Revised Date: 2023-01-13
Accepted Date: 2023-01-18

Available Online: 2023-04-13

Abstract

Abstract

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety. The task is challenging because the shadows on the pavement may have similar intensity with the crack, which interfere with the crack detection performance. Till to the present, there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows. To fill in the gap, we made several contributions as follows. First, we proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods. Second, we designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances. Third, we explored the mechanism of how shadows affect crack detection. Based on this mechanism, we propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes. Finally, we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall. We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset, and the experimental results demonstrate the superiority of our method.
- Automatic pavement crack detection,
- data augmentation compensation,
- deep learning,
- residual feature augmentation,
- shadow removal,
- shadow-crack dataset

FullText(HTML)

References(50)

References

[1]	L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, “Road crack detection using deep convolutional neural network,” in Proc. IEEE Int. Conf. Image Processing, Phoenix, USA, 2016, pp. 3708–3712.
[2]	Y. Shi, L. M. Cui, Z. Q. Qi, F. Meng, and Z. S. Chen, “Automatic road crack detection using random structured forests,” IEEE Trans. Intell. Trans. Syst., vol. 17, no. 12, pp. 3434–3445, Dec. 2016. doi: 10.1109/TITS.2016.2552248
[3]	S. J. Schmugge, L. Rice, J. Lindberg, R. Grizziy, C. Joffey, and M. C. Shin, “Crack segmentation by leveraging multiple frames of varying illumination,” in Proc. IEEE Winter Conf. Applications of Computer Vision, Santa Rosa, USA, 2017, pp. 1045–1053.
[4]	A. Benedetto, F. Tosti, L. Pajewski, F. D’Amico, and W. Kusayanagi, “FDTD simulation of the GPR signal for effective inspection of pavement damages,” in Proc. 15th Int. Conf. Ground Penetrating Radar, Brussels, Belgium, 2014, pp. 513–518.
[5]	Q. Zou, Y. Cao, Q. Q. Li, Q. Z. Mao, and S. Wang, “CrackTree: Automatic crack detection from pavement images,” Pattern Recognit. Lett., vol. 33, no. 3, pp. 227–238, Feb. 2012. doi: 10.1016/j.patrec.2011.11.004
[6]	N. Tanaka and K. Uematsu, “A crack detection method in road surface images using morphology,” in Proc. IAPR Workshop on Machine Vision Applications, Chiba, Japan, 1998, pp. 154–157.
[7]	Q. Q. Li and X. L. Liu, “Novel approach to pavement image segmentation based on neighboring difference histogram method,” in Proc. Congr. Image and Signal Processing, Sanya, China, 2008, pp. 792–796.
[8]	T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, Dec. 2006. doi: 10.1109/TPAMI.2006.244
[9]	M. Salman, S. Mathavan, K. Kamal, and M. Rahman, “Pavement crack detection using the Gabor filter,” in Proc. 16th Int. IEEE Conf. Intelligent Transportation Systems, The Hague, Netherlands, 2013, pp. 2039–2044.
[10]	A. Zhang, K. C. P. Wang, B. X. Li, E. H. Yang, X. X. Dai, Y. Peng, Y. Fei, Y. Liu, J. Q. Li, and C. Chen, “Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network,” vol. 32, no. 10, pp. 805–819, Oct. 2017.
[11]	P. P. Lu, C. Cui, S. B. Xu, H. E. Peng, and F. Wang, “SUPER: A novel lane detection system,” IEEE Trans. Intell. Veh., vol. 6, no. 3, pp. 583–593, Sept. 2021. doi: 10.1109/TIV.2021.3071593
[12]	F. Yang, L. Zhang, S. J. Yu, D. Prokhorov, X. Mei, and H. B. Ling, “Feature pyramid and hierarchical boosting network for pavement crack detection,” IEEE Trans. Intell. Trans. Syst., vol. 21, no. 4, pp. 1525–1535, Apr. 2020. doi: 10.1109/TITS.2019.2910595
[13]	K. Lu, “Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images,” in Proc. Conf. Computer Vision and Pattern Recognition, Virtual, 2020.
[14]	G. J. Wang, J. Wu, R. He, and B. Tian, “Speed and accuracy tradeoff for LiDAR data based road boundary detection,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1210–1220, Jun. 2021. doi: 10.1109/JAS.2020.1003414
[15]	L. Jiang, Y. Xie, and T. Ren, “A deep neural networks approach for pixel-level runway pavement crack segmentation using drone-captured images,” in Proc. Conf. Computer Vision and Pattern Recognition, Virtual, 2020.
[16]	H. Wang, Y. J. Yu, Y. F. Cai, X. B. Chen, L. Chen, and Y. C. Li, “Soft-weighted-average ensemble vehicle detection method based on single-stage and two-stage deep learning models,” IEEE Trans. Intell. Veh., vol. 6, no. 1, pp. 100–109, Mar. 2021. doi: 10.1109/TIV.2020.3010832
[17]	B. Bešić and A. Valada, “Dynamic object removal and spatio-temporal RGB-D inpainting via geometry-aware adversarial learning,” IEEE Trans. Intell. Veh., vol. 7, no. 2, pp. 170–185, Jun. 2022. doi: 10.1109/TIV.2022.3140654
[18]	W. Y. Liu, G. F. Ren, R. S. Yu, S. Guo, J. K. Zhu, and L. Zhang, “Image-adaptive YOLO for object detection in adverse weather conditions,” in Proc. 36th AAAI Conf. Artificial Intelligence, 2022, pp. 1792–1800.
[19]	H. Tian, T. Deng, and H. M. Yan, “Driving as well as on a sunny Day? Predicting driver’s fixation in rainy weather conditions via a dual-branch visual model” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1335–1338, Jul. 2022. doi: 10.1109/JAS.2022.105716
[20]	G. Volk, S. Müller, A. von Bernuth, D. Hospach, and O. Bringmann, “Towards robust CNN-based object detection through augmentation with synthetic rain variations,” in Proc. IEEE Intelligent Transportation Systems Conf., Auckland, New Zealand, 2019, pp. 285–292.
[21]	K. H. Liu, Z. H. Ye, H. Y. Guo, D. P. Cao, L. Chen, and F.-Y. Wang, “FISS GAN: A generative adversarial network for foggy image semantic segmentation,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1428–1439, Aug. 2021. doi: 10.1109/JAS.2021.1004057
[22]	J. Huyan, W. Li, S. Tighe, R. R. Deng, and S. Yan, “Illumination compensation model with k-means algorithm for detection of pavement surface cracks with shadow,” J. Comput. Civ. Eng., vol. 34, no. 1, p. 04019049, Jan. 2020. doi: 10.1061/(ASCE)CP.1943-5487.0000869
[23]	C. X. Guo, B. Fan, Q. Zhang, S. M. Xiang, and C. H. Pan, “AugFPN: Improving multi-scale feature learning for object detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2019, pp. 12592–12601.
[24]	E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640–651, Apr. 2017. doi: 10.1109/TPAMI.2016.2572683
[25]	X. W. Hu, C.-W. Fu, L. Zhu, J. Qin, and P.-A. Heng, “Direction-aware spatial context features for shadow detection and removal,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 11, pp. 2795–2808, Nov. 2020. doi: 10.1109/TPAMI.2019.2919616
[26]	S. N. Xie and Z. W. Tu, “Holistically-nested edge detection,” in Proc. IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 1395–1403.
[27]	M. A. Hedeya, E. Samir, E. El-Sayed, A. A. El-sharkawy, M. F. Abdel-Kader, A. Moussa, and R. F. Abdel-Kader, “A low-cost multi-sensor deep learning system for pavement distress detection and severity classification,” in Proc. 8th Int. Conf. Advanced Machine Learning and Technologies and Applications, Cairo, Egypt, 2022, pp. 21–33.
[28]	Y. Liu, M.-M. Cheng, X. W. Hu, J.-W. Bian, L. Zhang, X. Bai, and J. H. Tang, “Richer convolutional features for edge detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 8, pp. 1939–1946, Aug. 2019. doi: 10.1109/TPAMI.2018.2878849
[29]	J. S. Tang and Y. L. Gu, “Automatic crack detection and segmentation using a hybrid algorithm for road distress analysis,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Manchester, UK, 2013, pp. 3026–3030.
[30]	H. Oliveira and P. L. Correia, “CrackIT-An image processing toolbox for crack detection and characterization,” in Proc. IEEE Int. Conf. Image Processing, Paris, France, 2014, pp. 798–802.
[31]	M. D. Yan, S. B. Bo, K. Xu, and Y. Y. He, “Pavement crack detection and analysis for high-grade highway,” in Proc. 8th Int. Conf. Electronic Measurement and Instruments, Xi’an, China, 2007, pp. 4-548–4-552.
[32]	J. Zhou, P. S. Huang, and F.-P. Chiang, “Wavelet-based pavement distress detection and evaluation,” in Proc. SPIE of 5207, Wavelets: Applications in Signal and Image Processing X, San Diego, USA, 2003, pp. 728–739.
[33]	P. Subirats, J. Dumoulin, V. Legeay, and D. Barba, “Automation of pavement surface crack detection using the continuous wavelet transform,” in Proc. Int. Conf. Image Processing, Atlanta, USA, 2006, pp. 3037–3040.
[34]	M. Quintana, J. Torres, and J. M. Menéndez, “A simplified computer vision system for road surface inspection and maintenance,” IEEE Trans. Intell. Trans. Syst., vol. 17, no. 3, pp. 608–619, Mar. 2016. doi: 10.1109/TITS.2015.2482222
[35]	V. Baltazart, P. Nicolle, and L. Yang, “Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images,” in Proc. 25th European Signal Processing Conf., Kos, Greece, 2017, pp. 2016–2020.
[36]	T. S. Nguyen, S. Begot, F. Duculty, and M. Avila, “Free-form anisotropy: A new method for crack detection on pavement surface images,” in Proc. 18th IEEE Int. Conf. Image Processing, Brussels, Belgium, 2011, pp. 1069–1072.
[37]	C. Ieracitano, A. Paviglianiti, M. Campolo, A. Hussain, E. Pasero, and F. C. Morabito, “A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 64–76, Jan. 2021. doi: 10.1109/JAS.2020.1003387
[38]	P. Prasanna, K. J. Dana, N. Gucunski, B. B. Basily, H. M. La, R. S. Lim, and H. Parvardeh, “Automated crack detection on concrete bridges,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 591–599, Apr. 2016. doi: 10.1109/TASE.2014.2354314
[39]	X. Li, H. B. Duan, Y. L. Tian, and F.-Y. Wang, “Exploring image generation for UAV change detection,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1061–1072, Jun. 2022. doi: 10.1109/JAS.2022.105629
[40]	K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection,” Constr. Build. Mater., vol. 157, pp. 322–330, Dec. 2017. doi: 10.1016/j.conbuildmat.2017.09.110
[41]	J. Zhang, L. Pan, Q. L. Han, C. Chen, S. Wen, and Y. Xiang, “Deep learning based attack detection for cyber-physical system cybersecurity: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 377–391, Mar. 2022. doi: 10.1109/JAS.2021.1004261
[42]	K. Hacıefendioğlu and H. B. Başağa, “Concrete road crack detection using deep learning-based faster R-CNN method,” Iran. J. Sci. Technol.,Trans. Civ. Eng., vol. 46, no. 2, pp. 1621–1633, Apr. 2022. doi: 10.1007/s40996-021-00671-2
[43]	G. X. Hu, B. L. Hu, Z. Yang, L. Huang, and P. Li, “Pavement crack detection method based on deep learning models,” Wirel. Commun. Mob. Comput., vol. 2021, p. 5573590, May 2021.
[44]	H. Lv, C. Liu, X. W. Zhao, C. Y. Xu, Z. Cui, and J. Yang, “Lane marking regression from confidence area detection to field inference,” IEEE Trans. Intell. Veh., vol. 6, no. 1, pp. 47–56, Mar. 2021. doi: 10.1109/TIV.2020.3009366
[45]	L. L. Fan, H. W. Zhao, Y. Li, S. Li, R. Zhou, and W. B. Chu, “RAO-UNet: A residual attention and octave UNet for road crack detection via balance loss,” IET Intell. Trans. Syst., vol. 16, no. 3, pp. 332–343, Mar. 2022. doi: 10.1049/itr2.12146
[46]	L. Pauly, H. Peel, S. Luo, D. Hogg, and R. Fuentes, “Deeper networks for pavement crack detection,” in Proc. 34th Int. Symp. Autom. and Robotics in Construction, Taipei, China, 2017, pp. 479–485.
[47]	J. E. Hoffmann, H. G. Tosso, M. M. D. Santos, J. F. Justo, A. W. Malik, and A. U. Rahman, “Real-time adaptive object detection and tracking for autonomous vehicles,” IEEE Trans. Intell. Veh., vol. 6, no. 3, pp. 450–459, Sept. 2021. doi: 10.1109/TIV.2020.3037928
[48]	Y. X. Wang, S. Qiu, D. Li, C. D. Du, B.-L. Lu, and H. G. He, “Multi-modal domain adaptation variational autoencoder for EEG-based emotion recognition,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1612–1626, Sept. 2022. doi: 10.1109/JAS.2022.105515
[49]	J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F. F. Li, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, USA, 2009, pp. 248–255.
[50]	K. G. Zhang, Y. T. Zhang, and H.-D. Cheng, “CrackGAN: Pavement crack detection using partially accurate ground truths based on generative adversarial learning,” IEEE Trans. Intell. Trans. Syst., vol. 22, no. 2, pp. 1306–1319, Feb. 2021. doi: 10.1109/TITS.2020.2990703

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(9) / Tables(6)

Get Citation

PDF

XML

Article Metrics

Article views (1148) PDF downloads(178)

Highlights

This paper proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods
This paper designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances
This paper explored the mechanism of how shadows affect crack detection. Based on this mechanism, the authors propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes
This paper introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall

Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

doi: 10.1109/JAS.2023.123447

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content