Abstract
Three-dimensional target detection is a key technology in the fields of autonomous driving and robot control for applications such as self-driving cars and unmanned aircraft systems. In order to achieve high detection accuracy, this paper proposes a 3D target detection network with a coordinate attention training mechanism that generates voting feature points for better detection ability and an overlap region penalty mechanism that reduces false detection. In comparative experiments on public large-scale 3D datasets including the Scannet dataset and SUN-RGB-D dataset, the proposed method obtained an average detection accuracy mAP of 60.1% and 58.0% with an intersection ratio of 0.25, which demonstrates its superior effectiveness over the current main algorithms such as F-PointNet, VoxelNet and MV3D. The improved method is expected to achieve higher accuracy for 3D object detection relying only on point cloud information.
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Acknowledgments
The research was supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ21A040007), and Scientific Research Fund of Zhejiang Provincial Education Department (Grant No. Y201941856).
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Li, W., Zhu, S., Liu, H., Zhang, P., Zhang, X. (2023). Three-Dimensional Object Detection Network Based on Coordinate Attention and Overlapping Region Penalty Mechanisms. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14408. Springer, Cham. https://doi.org/10.1007/978-3-031-47665-5_7
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