Technology

Object Detection

Object Detection is the computer vision task that locates and classifies specific objects (e.g., cars, pedestrians) in images or video, using models like YOLO or Faster R-CNN to draw precise bounding boxes.

Object Detection employs deep learning algorithms, primarily Convolutional Neural Networks (CNNs), to perform two simultaneous tasks: object localization and classification. Unlike simple image classification, it draws a precise bounding box around each detected instance, identifying all objects within the frame (e.g., all 80 categories in the COCO dataset). Key architectures like the single-stage YOLO (You Only Look Once) prioritize speed for real-time applications (e.g., autonomous driving), while two-stage models like Faster R-CNN often deliver higher Mean Average Precision (mAP). This technology is critical for real-world systems: autonomous vehicles use it to track pedestrians and stop signs, and industrial quality control leverages it for defect identification on assembly lines.