CenterNet: Keypoint Triplets for Object Detection
This work addresses a key bottleneck in object detection for computer vision applications, offering an incremental improvement over existing keypoint-based methods.
The paper tackles the problem of incorrect bounding boxes in keypoint-based object detection by proposing CenterNet, which detects objects as triplets of keypoints, achieving an AP of 47.0% on MS-COCO and outperforming all existing one-stage detectors by at least 4.9%.
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing one-stage detectors by at least 4.9%. Meanwhile, with a faster inference speed, CenterNet demonstrates quite comparable performance to the top-ranked two-stage detectors. Code is available at https://github.com/Duankaiwen/CenterNet.