YOLOPoint Joint Keypoint and Object Detection
<|reference_start|>YOLOPoint Joint Keypoint and Object Detection: Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camera-based vehicle systems can use keypoints as well as objects as low- and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detects keypoints and objects in an image by combining YOLOv5 and SuperPoint to create a single forward-pass network that is both real-time capable and accurate. By using a shared backbone and a light-weight network structure, YOLOPoint is able to perform competitively on both the HPatches and KITTI benchmarks.<|reference_end|>
@article{backhaus2024yolopoint,
    title={YOLOPoint Joint Keypoint and Object Detection},
    author={Anton Backhaus, Thorsten Luettel, Hans-Joachim Wuensche},
    journal={Proceedings of Advanced Concepts for Intelligent Vision Systems,
  14124, 112-123 (2023)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03989},
    primaryClass={cs.CV}
}