Deep Learning for Crack-Like Object Detection - Kaige Zhang, Heng-Da Cheng

Deep Learning for Crack-Like Object Detection

Buch | Hardcover
100 Seiten
2023
CRC Press (Verlag)
978-1-032-18118-9 (ISBN)
57,35 inkl. MwSt
Accurately detecting crack localization is not an easy task. This book addresses important issues in detecting crack-like objects and provides a practical smart pavement surface inspection system using deep learning.
Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems.

This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed 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.

Kaige Zhang has a B.S. degree (2011) in electronic engineering from the Harbin Institute of Technology, China, and a Ph.D. degree (2019) in computer science from Utah State University, USA. 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 Trans. On T-IV, J. of Comput. in Civil Eng., Scientific Report, 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, Utah State University, Logan, UT. He has authored over 350 technical papers and is the Associate Editor of Pattern Recognition, Information Sciences, and New Mathematics and Natural Computation.

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.

Erscheinungsdatum
Zusatzinfo 11 Tables, black and white; 16 Line drawings, black and white; 34 Halftones, black and white; 50 Illustrations, black and white
Verlagsort London
Sprache englisch
Maße 138 x 216 mm
Gewicht 272 g
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Bauwesen
Technik Umwelttechnik / Biotechnologie
ISBN-10 1-032-18118-4 / 1032181184
ISBN-13 978-1-032-18118-9 / 9781032181189
Zustand Neuware
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