You Only Look Once: Unified, Real-Time Object Detection

Résumé : We present YOLO, a unified pipeline for object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is also extremely fast; YOLO processes images in real-time at 45 frames per second, hundreds to thousands of times faster than existing detection systems. Our system uses global image context to detect and localize objects, making it less prone to background errors than top detection systems like R-CNN. By itself, YOLO detects objects at unprecedented speeds with moderate accuracy. When combined with state-of-the-art detectors, YOLO boosts performance by 2-3% points mAP.