Deep Learning for Crack-Like Object Detection
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 9 October 2024
- ISBN 9781032181196
- Binding Paperback
- No. of pages106 pages
- Size 216x138 mm
- Weight 145 g
- Language English
- Illustrations 50 Illustrations, black & white; 34 Halftones, black & white; 16 Line drawings, black & white; 11 Tables, black & white 602
Categories
Short description:
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.
MoreLong 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.
MoreTable of Contents:
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.
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