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arXiv · 2402.03989

YOLOPoint Joint Keypoint and Object Detection

Anton Backhaus·Thorsten Luettel·Hans-Joachim Wuensche
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Abstract

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.

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Anton Backhaus, Thorsten Luettel, Hans-Joachim Wuensche. 2024-02-06. YOLOPoint Joint Keypoint and Object Detection. https://arxiv.org/abs/2402.03989

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