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Deep Learning for Crack-Like Object Detection



  • Available for pre-order on March 6, 2023. Item will ship after March 27, 2023
ISBN 9781032181189
March 27, 2023 Forthcoming by CRC Press
106 Pages 50 B/W Illustrations

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Book Description

Computer vision-based crack-like object detection has many useful applications, such as pavement surface inspection, underground pipeline inspection, bridge cracking monitoring, railway track assessment, etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried into complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, the deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. However, using deep learning for accurate crack localization is non-trivial.

This book discusses crack-like object detection problem in a comprehensive way. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. The book provides a comprehensive review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

Table of Contents

Introduction. Crack Detection with Deep Classification Network. Crack Detection with Fully Convolutional Network. Crack Detection with Generative Adversarial Learning. Self-Supervised Structure Learning for Crack Detection. Deep Edge Computing. Conclusion and Discussion.

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Author(s)

Biography

Kaige Zhang has a B.S. degree (2011) in electronic engineering from the Harbin Institute of Technology, China, a M.S. degree (2014) in signal and information processing from Harbin Engineering University, China, and a Ph.D. degree (2019) in computer science from Utah State University, USA.

He is currently a postdoctoral research associate at the University of Minnesota. He is a member of the editorial board for the journal Advances on International Computer Science. He is also a member of IEEE, and has served as session chair of IEEE international conference on Intelligent Transportation Systems 2018. His research interests include computer vision, machine learning, and the applications on intelligent transportation systems, precision agriculture, and biomedical data analytics.

Dr. Zhang has been the reviewer for many top journals in his research areas, such as IEEE Transactions on ITS, IEEE Transactions on T-IV, IEEE Access, Journal of Computing in Civil Engineering, International Journal of Pavement Research and Technology, Automation in Construction, Scientific Report, etc. He was also invited to review papers for many international conferences such as IEEE ITSC 2018, ECCV2018, IEEE CVPR 2019, etc.

Heng-Da Cheng has a Ph.D. in Electrical Engineering from Purdue University, West Lafayette, IN, USA in 1985 under the supervision Prof. K. S. Fu. He is a Full Professor with the Department of Computer Science, and an Adjunct Full Professor with the Department of Electrical Engineering, Utah State University, Logan, UT. He is an Adjunct Professor and a Doctorial Supervisor 98 with the Harbin Institute of Technology. He is also a Guest Professor with the Institute of Remote Sensing Application, Chinese Academy of Sciences, Wuhan University, and Shantou University, and a Visiting Professor of Northern Jiaotong University, Huazhong Science and Technology University, and Huanan Normal University.

He has authored over 350 technical papers and is the Co-Editor of the book entitled Pattern Recognition: Algorithms, Architectures, and Applications (World Scientific Publishing Company, 1991). Dr. Cheng is also an Associate Editor of Pattern Recognition, Information Sciences, and New Mathematics and Natural Computation.

Dr. Cheng was the General Chair of the 11th Joint Conference on Information Sciences (JCIS) (2008), the tenth JCIS (2007), the Ninth JCIS (2006), and the Eighth JCIS (2005). He served as a Program Committee Member and the Session Chair for many conferences, and as a Reviewer for many scientific journals and conferences. He has been listed in Who’s Who in the World, Who’s Who in America, and Who’s Who in Communications and Media.

His research interests include image processing, pattern recognition, computer vision, artificial intelligence, medical information processing, fuzzy logic, genetic algorithms, neural networks, parallel processing, parallel algorithms, and VLSI architectures.