Object Detection & YOLO in AI & Artificial Intelligence

While standard image classification outputs a single label for an entire image, Object Detection identifies multiple objects and their exact locations. This is achieved by predicting Bounding Boxes—sets of coordinates (x, y, width, height) that enclose each detected item. This dual task of 'Classification' and 'Localization' is what allows self-driving cars to distinguish between a pedestrian, a bicycle, and a stop sign simultaneously.

Before YOLO (You Only Look Once), object detection was slow, requiring models to scan thousands of 'region proposals' per image. YOLO reframed detection as a single regression problem. By dividing the image into an $S \times S$ grid, it predicts all bounding boxes and class probabilities in one forward pass. This extreme efficiency is why YOLO is the gold standard for real-time video applications, from security systems to live sports analytics.

Evaluating detection requires special tools. Intersection over Union (IoU) measures the overlap between predicted and actual boxes; a higher IoU indicates better localization. However, models often predict multiple boxes for the same object. To solve this, we use Non-Maximum Suppression (NMS), which suppresses (deletes) all boxes with lower confidence that overlap significantly with the best prediction, ensuring each object is counted only once.