Object Detection and Recognition Using Image Processing

Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Region-based CNNs (R-CNNs) have transformed real-time object identification by processing images rapidly while maintaining high accuracy. These models use anchor boxes and pyramid networks to recognize objects at various scales and aspect ratios.