Point cloud data annotation method, segmentation model determination method, target detection method and related equipment

A technology of target detection and point cloud data, applied in image data processing, image analysis, character and pattern recognition, etc., can solve problems such as low efficiency and inaccurate labeling results

Active Publication Date: 2019-09-20
CHANGSHA INTELLIGENT DRIVING INST CORP LTD
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Problems solved by technology

The current point cloud data labeling methods usually manually label point cloud data in thre

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  • Point cloud data annotation method, segmentation model determination method, target detection method and related equipment
  • Point cloud data annotation method, segmentation model determination method, target detection method and related equipment
  • Point cloud data annotation method, segmentation model determination method, target detection method and related equipment

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[0075] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0076] The point cloud data labeling method provided by this application can be applied to such as figure 1 shown in the application environment. The application environment involves the industrial computer and lidar in the vehicle's automatic driving system. The industrial computer obtains the original point cloud data collected by the lidar, performs two-dimensional labeling on the original point cloud data to obtain target labeling information, uses the trained target detection model to detect the original point cloud data to obtain target detection information, and th...

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Abstract

The invention relates to a point cloud data annotation method, a segmentation model determination method, a target detection method and related equipment. The annotation method comprises the steps of obtaining original point cloud data collected by a laser radar; projecting the original point cloud data to a two-dimensional graph, and obtaining each piece of target annotation information based on the two-dimensional graph; detecting the original point cloud data through a trained target detection model, and determining detected target detection information; and on the basis of the corresponding target category confidence, adopting target detection information to correct the target annotation information, obtaining a target detection data set of the original point cloud data, wherein the target detection data set comprises the position information and the target category of each target. By adopting the method, marking can be rapidly and accurately carried out.

Description

technical field [0001] The present application relates to the technical field of point cloud data processing, in particular to a point cloud data labeling, segmentation model determination, target detection method and related equipment. Background technique [0002] As an important environment perception sensor, lidar is widely used in the field of vehicle automatic driving. The point cloud data acquired by lidar contains various target information and can be used for obstacle detection. When training the model used for obstacle detection, it is generally necessary to use labeled point cloud data as training samples to optimize the obstacle detection algorithm. The current point cloud data labeling methods usually manually label point cloud data in three-dimensional space, which has the problems of low efficiency and inaccurate labeling results. Contents of the invention [0003] Based on this, it is necessary to provide a point cloud data labeling, segmentation model de...

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Application Information

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IPC IPC(8): G06T7/10G06K9/62
CPCG06T7/10G06T2207/10028G06T2207/20081G06T2207/10044G06F18/23G06F18/24
Inventor 曾钰廷徐琥
Owner CHANGSHA INTELLIGENT DRIVING INST CORP LTD
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