Deep Learning for Crack-Like Object Detection 1st Edition

Deep Learning for Crack-Like Object Detection, 1st Edition by Kaige Zhang and Heng‑Da Cheng is a practical, research-driven guide that brings cutting-edge computer vision to the world of structural inspection and asset management.

From the first page you’ll recognize the relevance: targeted solutions for detecting cracks, fissures, and other linear defects using modern deep learning techniques. The book explains core concepts clearly and moves quickly into applied methods—covering dataset preparation, annotation strategies, model architectures, training best practices, evaluation metrics, and deployment considerations—so professionals can translate theory into field-ready workflows.

Engineers, researchers, AI practitioners, and infrastructure managers will appreciate concise case studies and experiment results that showcase real-world performance across bridges, pavements, tunnels, pipelines, and urban structures. Emphasis on robustness, scalability, and transferability makes the content valuable whether you work in North America, Europe, Asia, or emerging smart-city projects worldwide.

Beyond algorithms, the text helps readers make operational decisions: how to choose models for high-resolution imagery, reduce false positives in noisy environments, and integrate automated detection into inspection pipelines. Clear visuals and examples guide readers through practical pitfalls and optimization strategies.

If you’re aiming to modernize inspection programs, accelerate research in crack-like object detection, or deploy reliable monitoring systems for critical infrastructure, this first edition is an essential resource. Order your copy today to bring advanced deep learning tools to your next inspection or research project.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.