In the ever-evolving field of computer vision, "Deep Learning for Object Detection and Localization" serves as an indispensable resource for researchers, practitioners, and students alike. This comprehensive book delves into the latest advancements and methodologies in deep learning, specifically tailored to enhance object detection and localization tasks. From foundational concepts to cutting-edge techniques, readers will embark on a journey through the intricacies of convolutional neural networks (CNNs), region-based frameworks, and advanced algorithms that power modern object detection systems. The purpose of writing this book is to bridge the knowledge gap in the dynamic field of object detection and localization using deep learning. As technology progresses, there is an increasing demand for robust and efficient systems capable of identifying and pinpointing objects within images and videos. Despite the plethora of resources available, there remains a need for a focused, in-depth guide that comprehensively covers both theoretical aspects and practical implementations. This book aims to fulfill that need by providing a detailed, structured approach to mastering the complexities of object detection and localization. Readers will benefit from the problem-solving focus of this book, which addresses key challenges faced in real-world applications. Whether it's enhancing accuracy in autonomous driving, improving precision in medical imaging, or optimizing performance in surveillance systems, the book offers practical solutions and insights. By exploring state-of-the-art techniques and real-world case studies, "Deep Learning for Object Detection and Localization" equips readers with the knowledge and tools necessary to tackle the pressing challenges in this rapidly advancing field.

master cutting edge object detection techniques with hands on guidance on different models for real world applications From theory to deployment Learn to build optimize and deploy deep learning models for real world applications Comprehensive coverage from basics to advanced frameworks like tensor flow and pytorch

Autorentext

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

Inhalt

"1.Introduction to Object Detection and Localization".- "2.Image Processing and Preprocessing".- "3.Object Detection Algorithms".- "4.Understanding Localization".- "5.Applications of Object Detection and Localization".- "6.Deep Learning Frameworks for Object Detection".- "7.Training and Fine-Tuning Object Detection Models".- "8.Challenges of Object Detection and Localization".- "10.Conclusion and Summary".