CenterNet is a deep learning object detection approach that builds upon keypoint-based detection but adds center keypoint prediction to address incorrect bounding boxes. The model uses a stacked hourglass backbone to predict center points, corners, and corner offsets simultaneously. On the MS-COCO dataset, it achieves 47.0% AP, surpassing all known one-stage detectors and approaching two-stage detector performance.