You only look once (YOLO)

Year: 2,016
Authors: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Journal: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Programming languages: Python

YOLO is a new approach to object detection. Prior work on object detection repurposed classifiers to perform detection. Here, object detection is framed 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. The unified architecture is extremely fast. The base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other realtime detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

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