Deep Learning Methods for Image Object Detection and Recognition | 62.55 MB
Title: Deep Learning Methods for Image Object Detection and Recognition
Author: Pengfei Shi
Category: Business & Finance, Industries & Professions, Information Management
Language: English | 258 Pages | ISBN: 9819833345
Description:
This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration-including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution-alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.
This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration-including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution-alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.
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