Abstract
In this paper, a novel methodology proposed for 3D detection with the purpose of boosting the detection accuracy and keep autonomous vehicles safety. The model takes the point clouds as the input directly. Based on our modified feature pyramid networks and VGG-16 named as FFPNets, which utilizes the one-stage fully convolutional network to detect 3D cars. The experimental result shows the robustness of the model and its superiority. The average precision (AP) of the car for easy, moderate, and hard cases achieves state-of-the-art detection accuracy on KITTI datasets.
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Wen, L., Jo, KH. (2019). Fully Convolutional Neural Networks for 3D Vehicle Detection Based on Point Clouds. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_56
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