Volume List  / Volume 8 (1)



DOI: 10.7708/ijtte.2018.8(1).01

8 / 1 / 1-14 Pages


Kasthurirangan Gopalakrishnan - Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA -

Hoda Gholami - InfraDrone LLC, Des Moines, Iowa, USA -

Akash Vidyadharan - InfraDrone LLC, Des Moines, Iowa, USA -

Alok Choudhary - Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA -

Ankit Agrawal - Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA -


The field of computer vision based civil infrastructure defect detection is constantly evolving with steady advances being made in sensing technologies, hardware, and image processing techniques. Although a number of image processing techniques have been proposed over the years with varying degrees of success, real-world situations (e.g., lack of background illumination, shadow changes) pose a significant challenge to the wide adoption of such techniques, especially for routine analysis. With the emerging application of Unmanned Aerial Vehicles (UAVs) or drones for civil infrastructure inspection and condition monitoring, an added level of complexity is introduced in vision-based crack detection as the process of acquiring images using UAVs is not yet standardized resulting in images of widely varying sizes, resolutions, blurriness, etc. In recent years, Deep Learning, a generalized form of Deep Neural Network (DNN) algorithms that can learn very complex mappings between inputs and outputs directly from the data, has achieved huge success in diverse fields such as automatic speech recognition, image recognition, Natural Language Processing (NLP), drug and materials discovery, etc. However, the large number of hidden neurons and layers used in DNNs result in computationally-intensive matrix and vector computations involving millions of parameters, requiring the use of high-performance computing systems. Also, it is practically impossible to get labeled “big data” samples in many domains to be able to train an entire DNN from scratch. In such situations, the use of a pre-trained deep learning model and fine-tuning it to the novel task at hand with smaller datasets, has shown to be successful across domains. In this paper, we propose the use of pre-trained deep learning models with transfer learning for crack damage detection in UAV images of civil infrastructure. The robustness of the proposed approach is tested on a small set of real-world, complex UAV-sourced infrastructure images not used during training and validation. The results show that the proposed method can rapidly and easily achieve up to 90% accuracy in finding cracks in realistic situations without any augmentation and preprocessing.

Download Article

Number of downloads: 1958


This work is supported in part by the following grants: AFOSR award FA9550-12-1-0458; NIST award 70NANB14H012; NSF award CCF-1409601; DOE awards DE-SC0007456, DE-SC0014330.


Agrawal, A.; Choudhary, A. 2016. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science, APL Materials 4(5): 1-9.


ASCE. 2017. American Society of Civil Engineers (ASCE) 2017 Infrastructure Report Card: Roads. Available from internet: https://www.infrastructurereportcard.org/.


Bai, S. 2017. Growing random forest on deep convolutional neural networks for scene categorization, Expert Systems with Applications 71: 279–287.


Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H., 2015. Chest pathology detection using deep learning with non-medical training. In Proceedings of the IEEE 12th International Symposium on Biomedical Imaging (ISBI), 294–297.


Carrio, A.; Sampedro, C.; Rodriguez-Ramos, A.; Campoy, P. 2017. A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles, Journal of Sensors 2017(3296874): 1-13.


Chollet, F. 2015. keras. GitHub. Available from internet: https://keras.io/.


Deng, J.; Dong, W.; Socher, R.; Li, L.; Li, K.; Fei-fei, L. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 248-255.


Ellenberg, A.; Kontsos, A.; Bartoli, I.; Pradhan, A. 2014. Masonry Crack Detection Application of an Unmanned Aerial Vehicle. In Proceedings of the 2014 International Conference on Computing in Civil and Building Engineering, 1788–1795.


Floreano, D.; Wood, R.J. 2015. Science, technology and the future of small autonomous drones, Nature 521: 460–466.


Geurts, P.; Ernst, D.; Wehenkel, L. 2006. Extremely randomized trees, Machine Learning 63: 3–42.


Goh, H.; Thome, N.; Cord, M.; Lim, J.H. 2014. Learning Deep Hierarchical Visual Feature Coding, IEEE Transactions on Neural Networks and Learning Systems 25: 2212–2225.


Gopalakrishnan, K.; Khaitan, S.K.; Choudhary, A.; Agrawal, A. 2017. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection, Construction and Building Materials 157: 322–330.


Hinton, G.E.; Osindero, S.; Teh, Y.-W. 2006. A Fast Learning Algorithm for Deep Belief Nets, Neural Computing 18: 1527–1554.


Lee, J.; Wang, J.; Crandall, D.; Šabanović, S.; Fox, G. 2017. Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles. In Proceedings of the 2017 First IEEE International Conference on Robotic Computing (IRC), 36–43.


Liu, W.; Wang, Z.; Liu, X.; Zeng, N.; Liu, Y.; Alsaadi, F.E. 2017. A survey of deep neural network architectures and their applications, Neurocomputing 234: 11–26.


Pal, S.; 2016. Transfer Learning and Fine Tuning for Cross Domain Image Classification with Keras. GitHub. Available from internet: https://github.com/sujitpal/fttl-with-keras.


Pereira, F.C.; Pereira, C.E. 2015. Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs. In Proceedings of the 2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics (CESCIT 2015), 16–21.


Sankarasrinivasan, S.; Balasubramanian, E.; Karthik, K.; Chandrasekar, U.; Gupta, R. 2015. Health Monitoring of Civil Structures with Integrated UAV and Image Processing System. In Proceedings of the Eleventh International Conference on Image and Signal Processing (ICISP 2015), 508–515.


Sarkar, S.; Reddy, K.; Giering, M.; Gurvich, M. 2016. Deep Learning for Structural Health Monitoring: A Damage Characterization Application. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, 1-7.


Shin, H.-C.; Roth, H.R.; Gao, M.; Lu, L.; Xu, Z.; Nogues, I.; Yao, J.; Mollura, D.; Summers, R.M. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE transactions on medical imaging 35: 1285–1298.


Simonyan, K.; Zisserman, A. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition, In Proceedings of the 2015 International Conference on Learning Representation (ICLR 2015), 1-14.


Tajbakhsh, N.; Shin, J.Y.; Gurudu, S.R.; Hurst, R.T.; Kendall, C.B.; Gotway, M.B.; Liang, J. 2016. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? In Proceedings of the IEEE Transactions on Medical Imaging, 35: 1299–1312.


Vidyadharan, A.; Carter, T.; Ceylan, H.; Bloebaum, C.; Gopalakrishnan, K.; Kim, S. 2017. Civil Infrastructure Health Monitoring and Management Using Unmanned Aerial Systems. In Proceedings of the 2017 International Conference on Highway Pavements and Airfield Technology, 1-8.


Xie, D.; Zhang, L.; Bai, L. 2017. Deep Learning in Visual Computing and Signal Processing, Applied Computational Intelligence and Soft Computing 2017(e1320780): 1-13.


Yokoyama, S.; Matsumoto, T. 2017. Development of an Automatic Detector of Cracks in Concrete Using Machine Learning. In Proceedings of The 3rd International Conference on Sustainable Civil Engineering Structures and Construction Materials, 1250–1255.