Deep Learning for Crack-Like Object Detection

Kaige Zhang, Heng-Da Cheng (Utha State Uni.)

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Hardback

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English

CRC Press
20 March 2023

Automatic control engineering; Civil engineering, surveying & building; Environmental science, engineering & technology; Neural networks & fuzzy systems

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.

By: Kaige Zhang, Heng-Da Cheng (Utha State Uni.)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 216mm, Width: 138mm,
Weight: 272g
ISBN: 9781032181189
ISBN 10: 1032181184
Pages: 100
Publication Date: 20 March 2023
Audience: General/trade , College/higher education , ELT Advanced , Primary
Format: Hardback
Publisher's Status: Active

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.