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Intelligent Automated Defect Detection

20-10-2026, 07:41, posted by creativelivenew1

Intelligent Automated Defect Detection
English | 2026 | ISBN: 1041287976 | 262 pages | True PDF,EPUB | 101.66 MB
This book presents cutting-edge research on the integration of artificial intelligence, deep learning, and computer vision with traditional nondestructive testing (NDT) methods. It compiles recent advances in automated defect detection across critical infrastructure and industrial applications.Intelligent Automated Defect Detection: AI Solutions for Roads, Welding, and Industrial Inspectiondemonstrates how AI-driven solutions are transforming quality inspection from subjective, time-intensive manual processes into real-time, scalable, and objective automated systems. The book provides comprehensive coverage of machine learning applications in civil infrastructure inspection and industrial welding quality assurance, utilizing radiography, ultrasonic B-scans, and time-series sensing. The authors present state-of-the-art deep learning architectures including convolutional neural networks, transformer models, and foundation models like the Segment Anything Model (SAM), alongside practical guidance on developing explainable AI systems and overcoming data scarcity challenges. The book offers proven methodologies for transitioning from proof-of-concept studies to robust real-world applications that enhance inspection efficiency while reducing costs and failure risks. Real-world case studies are included throughout, with additional applications available on the authors' GitHub page.This book is intended for researchers, engineers, and practitioners working at the intersection of AI, NDT, and industrial quality assurance. It also serves as a resource for graduate students and early-career professionals interested in applying modern data-driven approaches to complex engineering problems.


This book presents cutting-edge research on the integration of artificial intelligence, deep learning, and computer vision with traditional nondestructive testing (NDT) methods. It compiles recent advances in automated defect detection across critical infrastructure and industrial applications.
Intelligent Automated Defect Detection: AI Solutions for Roads, Welding, and Industrial Inspectiondemonstrates how AI-driven solutions are transforming quality inspection from subjective, time-intensive manual processes into real-time, scalable, and objective automated systems. The book provides comprehensive coverage of machine learning applications in civil infrastructure inspection and industrial welding quality assurance, utilizing radiography, ultrasonic B-scans, and time-series sensing. The authors present state-of-the-art deep learning architectures including convolutional neural networks, transformer models, and foundation models like the Segment Anything Model (SAM), alongside practical guidance on developing explainable AI systems and overcoming data scarcity challenges. The book offers proven methodologies for transitioning from proof-of-concept studies to robust real-world applications that enhance inspection efficiency while reducing costs and failure risks. Real-world case studies are included throughout, with additional applications available on the authors' GitHub page.
Intelligent Automated Defect Detection: AI Solutions for Roads, Welding, and Industrial Inspection
demonstrates how AI-driven solutions are transforming quality inspection from subjective, time-intensive manual processes into real-time, scalable, and objective automated systems. The book provides comprehensive coverage of machine learning applications in civil infrastructure inspection and industrial welding quality assurance, utilizing radiography, ultrasonic B-scans, and time-series sensing. The authors present state-of-the-art deep learning architectures including convolutional neural networks, transformer models, and foundation models like the Segment Anything Model (SAM), alongside practical guidance on developing explainable AI systems and overcoming data scarcity challenges. The book offers proven methodologies for transitioning from proof-of-concept studies to robust real-world applications that enhance inspection efficiency while reducing costs and failure risks. Real-world case studies are included throughout, with additional applications available on the authors' GitHub page.
This book is intended for researchers, engineers, and practitioners working at the intersection of AI, NDT, and industrial quality assurance. It also serves as a resource for graduate students and early-career professionals interested in applying modern data-driven approaches to complex engineering problems.

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