Deep Learning for Crack-Like Object Detection

Deep Learning for Crack-Like Object Detection

EnglishPaperback / softbackPrint on demand
Zhang, Kaige
Taylor & Francis Ltd
EAN: 9781032181196
Print on demand
Delivery on Monday, 13. of July 2026
CZK 513
Common price CZK 570
Discount 10%
pc
Do you want this product today?
Megabooks Praha Korunní
not available
Librairie Francophone Praha Štěpánská
not available
Megabooks Ostrava
not available
Megabooks Olomouc
not available
Megabooks Plzeň
not available
Megabooks Brno
not available
Megabooks Hradec Králové
not available
Megabooks České Budějovice
not available
Megabooks Liberec
not available

Detailed information

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.

EAN 9781032181196
ISBN 1032181192
Binding Paperback / softback
Publisher Taylor & Francis Ltd
Publication date October 9, 2024
Pages 100
Language English
Dimensions 216 x 138
Country United Kingdom
Authors Cheng Heng-Da; Zhang, Kaige
Manufacturer information
The manufacturer's contact information is currently not available online, we are working intensively on the axle. If you need information, write us on [email protected], we will be happy to provide it.