What Is Object Detection? How It Works and Why It Matters

Object detection, a computer vision solution that identifies objects and their locations in images, is revolutionizing various industries by enabling applications that were previously impossible. It works by training models with labeled images to recognize specific objects and their coordinates, returning a confidence level for each prediction. This technology is widely used in fields such as sports analytics, transportation safety, food manufacturing, and autonomous vehicles, where it can detect obstacles, ensure product integrity, and allow vehicles to make informed decisions. Popular object detection architectures include Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) family, with YOLOv5 and YOLOv8 being particularly notable for their efficiency and widespread use. Object detection is distinct from image classification, which only assigns a single label to an image, and from image segmentation, which identifies objects at a pixel level. Tools like Roboflow make it easier to build and deploy object detection models, offering resources for labeling, training, and testing models across various platforms.