Deep Learning for Crack-Like Object Detection (Hardcover)

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.

General

Imprint:	Taylor & Francis
Country of origin:	United Kingdom
Release date:	March 2023
First published:	2023
Authors:	Kaige Zhang • Heng-Da Cheng
Dimensions:	216 x 138mm (L x W)
Format:	Hardcover
Pages:	100
ISBN-13:	978-1-03-218118-9
Categories:	Books > Professional & Technical > Civil engineering, surveying & building > General Books > Professional & Technical > Environmental engineering & technology > General Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General Promotions
LSN:	1-03-218118-4
Barcode:	9781032181189

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