Three-dimensional target detection method and system based on substream sparse convolution

A technology of three-dimensional target and detection method, which is applied in the field of three-dimensional target detection based on sub-stream sparse convolution, can solve the problems of large memory space and low efficiency, and achieves a combination of accuracy, guaranteeing time efficiency, and improving efficiency and precision. Effect

Pending Publication Date: 2021-01-26
WUHAN UNIV OF SCI & TECH
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[0007] The present invention provides a three-dimensional object detection method and system based on subflow sparse convolution, which is used to solve t

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  • Three-dimensional target detection method and system based on substream sparse convolution
  • Three-dimensional target detection method and system based on substream sparse convolution
  • Three-dimensional target detection method and system based on substream sparse convolution

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[0039] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0040] The embodiment of the present invention aims to solve the problems of poor performance of the PVCNN method in the local feature extraction part, time efficiency and large GPU memory usage, resulting in low target detection efficiency and low precision.

[0041] Referring to the sub-flow sparse convolution, a faster and less memory-occupied PVCNN improvement method is proposed, and the ...

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Abstract

The invention provides a three-dimensional target detection method and system based on substream sparse convolution. The method comprises the following steps: obtaining initial point cloud data of a target scene; based on the initial point cloud data and the optimized point cloud voxel convolutional neural network, obtaining target point cloud features, and obtaining the optimized point cloud voxel convolutional neural network by optimizing the point cloud voxel convolutional neural network through substream sparse convolution; and performing target detection on the target scene according to the target point cloud features. According to the embodiment of the invention, the point cloud voxel convolutional neural network extraction method based on substream sparse convolution is constructed,further feature extraction is carried out by using the sparsity of the point cloud, and deep extraction can be carried out on local information more efficiently so that the efficiency and precision of three-dimensional target detection are improved.

Description

technical field [0001] The invention relates to the technical fields of robots and computer vision, in particular to a three-dimensional object detection method and system based on subflow sparse convolution. Background technique [0002] In the field of autonomous driving and robotics, it is often necessary to directly acquire and process 3D data information. 3D data information has more depth information than ordinary 2D data information. These depth information can eliminate a large number of segmentation uncertainties in 2D images. And can provide important geometric information. [0003] Since the 1990s, the 3D shape feature extraction algorithm has experienced more than 20 years of development, gradually transitioning from manual feature extraction to deep learning. There are four main ways for deep learning to be used in 3D: (a) multi-view-based convolution, (b) voxel-based convolution, (c) structured convolution and (d) direct convolution of point clouds . Among t...

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Application Information

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IPC IPC(8): G06K9/46G06T7/00G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/10028G06T2207/20081G06V10/44G06V10/462G06N3/045
Inventor 林云汉孙亚兰刘双元闵华松叶亮左韬邓春华
Owner WUHAN UNIV OF SCI & TECH
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