YOLOv3 is an object detection algorithm developed by Joseph Redmon that performs real-time detection by predicting bounding boxes and class probabilities directly from full images in a single evaluation pass. It introduces multiscale predictions using three different detection kernel sizes, achieving 28.2 mAP on the COCO dataset while running at 22 milliseconds per frame on 320×320 input.