
Deep Learning for Crack-Like Object Detection
CRC Press
1st Edition
Published on 20. March 2023
Book
Hardback
100 pages
978-1-032-18118-9 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, General, and Postgraduate
Illustrations
50 s/w Abbildungen, 34 s/w Photographien bzw. Rasterbilder, 16 s/w Zeichnungen, 11 s/w Tabellen
11 Tables, black and white; 16 Line drawings, black and white; 34 Halftones, black and white; 50 Illustrations, black and white
Dimensions
Height: 222 mm
Width: 145 mm
Thickness: 10 mm
Weight
278 gr
ISBN-13
978-1-032-18118-9 (9781032181189)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Kaige Zhang | Heng-da Cheng
Deep Learning for Crack-Like Object Detection
Book
10/2024
1st Edition
CRC Press
€31.70
Shipment within 10-20 days

Kaige Zhang | Heng-da Cheng
Deep Learning for Crack-Like Object Detection
E-Book
03/2023
1st Edition
CRC Press
€25.99
Available for download

Kaige Zhang | Heng-da Cheng
Deep Learning for Crack-Like Object Detection
E-Book
03/2023
1st Edition
CRC Press
€25.99
Available for download
Persons
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.
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.
Content
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.