Object Detection with Deep Learning Models Principles and Applications

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.

The book offers a rich blend of theory and practice. 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. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Features:

• A structured overview of deep learning in object detection
• A diversified collection of applications of object detection using deep neural networks
• Emphasize agriculture and remote sensing domains
• Exclusive discussion on moving object detection

1. Introduction: Deep Learning and Computer Vision. 2. Object Detection Frameworks and Services in Computer Vision. 3. Real Time Tracing and Alerting System for Vehicles and Children To Ensure Safety and Security Using Labview. 4. Mobile Application based Assistive System for Visually Impaired People: A Hassle-free Shopping Support System. 5. Traffic Density and on-road Moving Object Detection Mangement using Video Processing. 6. Automate Vehicle Number Plate Recognition System using Convolution Long Short-term memory technique. 7. Deep Learning based Indian Vehicle Number Plater Detection and Recognitoin. 8. Smart Diabetes System using CNN in Health Data Analytics. 9. Independent Automobile Intelligent Motion Controller and Re-direction using Deep Learning system. 10. Deep Learning Solutions for Pest Detection. 11. Deep Learning Solutions for Pest Identification in Agriculture. 12. A complete Framework for LULC Classification of Madurai Remote Sensing Image with Deep Learning based Fusion Technique. 13. Human Behavioural Identifiers - A detailed discussion by T.SubaNachiar, T.Shanmuga Priya, P.R.Hemalatha, J.V Anchitaalagammai

Poonkuntran Shanmugam earned a BE degree in Information Technology from Bharathidasan University, Tiruchirapalli, India; and MTech and PhD degrees in Computer and Information Technology from Manonmaniam Sundaranar University, Tirunelveli, India. He is presently with VIT Bhopal University, Madhya Pradesh, India as Professor & Dean for the School of Computing Science and Engineering. He has more than a decade of experience in teaching and research and successfully executed three funded research grant projects from the Indian Space Research Organization, Defense Research Development Organization, and Ministry of New and Renewable Energy, Government of India, to the tune of 1.10 Crores. He received two seminar grants from Anna University, Chennai, and the All India Council for Technical Education-Indian Society for Technical Education for the tune of 4 Lacs. He has published more than 80 technical publications, authored 6 books and 2 chapters. He is the recipient of Cognizant Best Faculty Award 2017–18 and served as a State Level Student Coordinator for Region VII, CSI, India in 2016–17. He is a lifetime member of IACSIT, Singapore, CSI, India, and ISTE, India. His research areas of interests include information security, computer vision, artificial intelligence, and machine learning.

Dr Rajesh Kumar Dhanraj is a Professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He earned a BE degree in Computer Science and Engineering from the Anna University Chennai, India in 2007, then an MTech from the Anna University Coimbatore, India in 2010 and a PhD in Computer Science from Anna University, Chennai, India, in 2017. He has contributed to 30+ authored and edited books on various technologies, 21 Patents and 53 articles and papers in various refereed journals and international conferences and contributed chapters to books. His research interests include Machine Learning, Cyber-