Deep Vision-An Image Processing with deep Learning

This book Deep Vision is designed to serve as a comprehensive guide for students educators and practitioners in computer science data science and artificial intelligence. It presents the core concepts techniques and tools essential for understanding and implementing deep learning models for vision-based tasks. The book begins with foundational topics such as image classification and feature extraction introducing the reader to Convolutional Neural Networks (CNNs) and their building blocks. It further explores popular and efficient architectures like VGG ResNet MobileNet and EfficientNet. Advanced chapters cover object detection algorithms including YOLO SSD and Faster R-CNN as well as segmentation techniques like U-Net and Mask R-CNN supplemented by appropriate evaluation metrics such as IoU mAP and Dice coefficient. A dedicated section on transfer learning model optimization and deployment illustrates how to build real time efficient systems using formats like ONNX and TFLite across platforms such as mobile web and edge devices. Case studies and real-world examples throughout the book aim to bridge theoretical concepts with practical applications.