Year: 2024
Type: article
Abstract: Here we present a comparison of two different deep learning architectures’ effectiveness along with two distinct detection head approaches for detecting point cloud ball objects. Two backbones that are explored are: VoxelNet, suited for organized point clouds, and PointNet, which handles unorganized point clouds. We modified and implemented SSD and Faster R-CNN detection heads for both backbones. It turns out that the PointNet backbone integrated with a customized Faster R-CNN detection head achieved higher accuracy compared to other combination of models.
Author: Minjinsor Myagmarsuren
Institution: Mongolian Academy of Sciences
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414190820
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