Deep Learning for Object Detection and Localization - Hardcover

Über die Autorin bzw. den Autor

Dr. Abdussalam Elhanashi is a Senior Researcher at the Università di Pisa, Italy, specializing in advanced applications of deep learning and video image processing. He holds an M.Sc. in Electronics and Electrical Engineering from the University of Glasgow, Scotland, and an MBA from the University of Nicosia, Cyprus. He earned his Ph.D. in Information Engineering from the Università di Pisa, Italy funded by a prestigious merit-based scholarship from the Islamic Development Bank (IsDB) as Libya’s top candidate for 2019–2020. Dr. Elhanashi was ranked among Stanford University and Elsevier’s World’s Top 2% Scientists for 2025. He has authored and co-authored numerous scientific articles and academic books indexed in Scopus and the Web of Science. In 2021, he served as a Research Fellow at the University of Strathclyde, applying deep learning models to analyse CT scans and X-ray images for medical diagnostics. In 2022, he was a Visiting Researcher at Hiroshima University, Japan, focusing on advanced video analysis techniques. With over 16 years of industry experience, Dr. Elhanashi has successfully managed engineering projects, conducted system maintenance, and performed root cause analyses to address complex technical challenges. He is also a Developer at the Society for Imaging Informatics in Medicine (SIIM), USA. His research interests include real-world AI applications, lightweight model development, video surveillance, IoT-based low-cost embedded systems, AI-driven solutions for medical imaging, and efficient coding techniques for image and video processing systems.

Prof. Sergio Saponara, Director of the Department of Information Engineering at the University of Pisa, IEEE Distinguished Lecturer, obtained his Master's degree cum laude and Ph.D. in Electronic Engineering from the University of Pisa. In 2002, he was a Marie Curie Research Fellow at the Inter-university Microelectronics Center (IMEC) in Leuven, Belgium. He was also a post-graduate researcher at the National Research Council. Currently, he is a Full Professor of Electronics at the University of Pisa, where he teaches courses in Design of IoT Systems, Electronic Systems for Robotics, HW and Embedded Security for the Master's degrees in Electronic Engineering, Robotics and Automation Engineering, and Cybersecurity. Additionally, he teaches Electronics at the Italian Naval Academy in Livorno. Prof. Saponara was President of the Bachelor's and Master's programs in Electronic Engineering at the University of Pisa. He has co-authored 600 scientific articles indexed in Scopus and 20 patents. He is a Founding Member of the IoT CASS SiG and has been a Program Committee Member for over 100 international IEEE and SPIE

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Deep Learning for Object Detection and Localization offers a comprehensive and structured exploration of modern computer vision techniques, combining theoretical foundations with hands-on implementation. Written for researchers, engineers, and students, this book bridges the gap between algorithmic understanding and real-world deployment, providing a complete roadmap from data preparation to model optimization and deployment. Readers are guided through the fundamentals of image processing, data annotation, and augmentation before delving into cutting-edge detection architectures such as YOLO, SSD, EfficientDet, and R-CNN variants. The book also covers localization strategies, model selection, framework comparisons, and advanced optimization for embedded and cloud-based applications. With dedicated chapters on transfer learning, quantization, and deployment on edge devices, it provides practical solutions to meet real-time and resource-efficient constraints. By integrating case studies in autonomous driving, medical imaging, and robotics, “Deep Learning for Object Detection and Localization” equips readers with the knowledge and tools to design, train, and deploy intelligent vision systems that balance accuracy, efficiency, and scalability.

Key Features

· Comprehensive coverage from deep learning fundamentals to advanced object detection and localization techniques.

· Practical workflow guidance including data annotation, preprocessing, augmentation, and model evaluation.

· Detailed exploration of modern architectures such as YOLO, SSD, EfficientDet, and Mask R-CNN.

· Focus on real-world deployment, covering model compression, quantization, and edge/cloud implementation.

· Rich case studies demonstrating applications in autonomous driving, medical imaging, and robotics.