We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to per-form 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.
Object Detection is the process of finding and recognizing real-world object instances.
Technology and Tools:
Ai | Image Processing | Machine Learning | Deep Learning | Python | OpenCv | Raspberry Pi | Vnc Viewer | L298 Motor Driver | USB Camera