Object detection

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Object detection

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Object detection is a fundamental task in computer vision that involves identifying and locating objects within images or videos. Unlike simple image classification which only predicts what objects are present, object detection provides both the object categories and their precise locations through bounding boxes.

Modern object detection systems typically rely on deep learning models, especially convolutional neural networks (CNNs). Popular architectures like Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) have revolutionized the field by balancing speed and accuracy. The process generally involves extracting features from the input image, proposing potential regions of interest, and then classifying those regions while refining their bounding box coordinates.

Object detection has wide-ranging applications from autonomous vehicles identifying pedestrians to retail systems tracking inventory. Current challenges include improving performance on small objects, handling occlusions, and reducing computational costs for real-time processing.