Object Detection with Deep Learning Models: Principles and Applications

Object Detection with Deep Learning Models is a comprehensive book that provides a systematic and methodical overview of recent advances in object detection and recognition using deep learning methods. It is suitable for students, researchers, and practitioners interested in deep learning, computer vision, and beyond, and can also be used as a reference book.

Format: Hardback
Length: 266 pages
Publication date: 01 November 2022
Publisher: Taylor & Francis Ltd

Object Detection with Deep Learning Models delves into the latest advancements in object detection and recognition through deep learning techniques, which have achieved remarkable success in the realm of computer vision and image processing. This comprehensive book provides a systematic and methodical exploration of the latest developments in deep learning theory and its practical applications in computer vision, showcasing them through key topics such as object detection, face analysis, 3D object recognition, and image retrieval.

The book offers a harmonious blend of theory and practice, making it an ideal resource for students, researchers, and practitioners interested in deep learning, computer vision, and related fields. Additionally, it serves as a valuable reference book for those seeking to deepen their understanding of machine learning and calculus while exploring the vast applications of object detection in various domains, including agriculture and remote sensing.

Key Features:

Structured Overview of Deep Learning in Object Detection: This book offers a comprehensive guide to deep learning in object detection, providing a clear roadmap for understanding the fundamental concepts and methodologies. It covers essential topics such as convolutional neural networks, feature extraction, and classification algorithms, enabling readers to grasp the theoretical foundations of deep learning and apply them to real-world scenarios.

Diverse Collection of Applications of Object Detection Using Deep Neural Networks: The book showcases a wide range of applications of object detection using deep neural networks. It explores various industries, including autonomous vehicles, medical imaging, surveillance systems, and agriculture, highlighting the practical benefits and challenges of deploying deep learning models in these domains. Readers will gain insights into how deep learning algorithms are used to identify and track objects, extract meaningful features, and make accurate predictions.

Emphasis on Agriculture and Remote Sensing Domains: The book emphasizes the importance of object detection in agriculture and remote sensing applications. It discusses the challenges faced by farmers in monitoring crop health, identifying pests, and optimizing irrigation practices. Additionally, it explores the use of deep learning in remote sensing for land cover mapping, environmental monitoring, and disaster response.

Exclusive Discussion on Moving Object Detection: Moving object detection is a crucial aspect of computer vision, particularly in applications such as traffic surveillance, sports analytics, and autonomous vehicles. This book provides an in-depth discussion on moving object detection techniques, including background subtraction, optical flow, and deep learning-based methods. Readers will learn how to detect and track moving objects in complex environments, extracting valuable information for various applications.

In conclusion, Object Detection with Deep Learning Models is a comprehensive and authoritative guide to the latest developments in object detection and recognition through deep learning techniques. It offers a rich blend of theory and practice, making it suitable for students, researchers, and practitioners in the field of computer vision and beyond. By exploring a diverse range of applications and emphasizing the importance of moving object detection, this book provides valuable insights and practical knowledge for those seeking to leverage the power of deep learning in their research and applications.

Dimension: 254 x 178 (mm)
ISBN-13: 9781032074009