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Image labeling method and device, computer equipment and storage medium

An image annotation and computer program technology, applied in the field of image processing, can solve the problems of long time-consuming image annotation and large human resources

Active Publication Date: 2022-04-08
禾多科技(北京)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, labeling hundreds of thousands of images requires a lot of human resources and time-consuming image labeling

Method used

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  • Image labeling method and device, computer equipment and storage medium
  • Image labeling method and device, computer equipment and storage medium
  • Image labeling method and device, computer equipment and storage medium

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Embodiment Construction

[0061] 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.

[0062] In one embodiment, such as figure 1 As shown, an image tagging method is provided. In this embodiment, the method is applied to a terminal for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server. The interactive realization of terminal and server. In this embodiment, the method includes the following steps:

[0063] Step 102, obtaining a labeled data set.

[0064] Among them, the labeled data set includes point cloud data with a preset number of frames.

[0065]...

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Abstract

The invention relates to an image annotation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring an annotation data set, wherein the annotation data set comprises point cloud data of a preset frame number; inputting a preset frame number of point cloud data into a detection frame generation model; performing feature processing on each frame of point cloud data through a detection frame generation model, generating a feature enhancement sequence of a target labeling object in the point cloud data, and performing attribute regression processing on the feature enhancement sequence to obtain a target detection frame of the target labeling object in each frame of point cloud data; and fitting estimation is carried out on a preset frame number of target detection frames to obtain the target detection frames with the same frame number as the image data, the target detection frames are projected to the image data corresponding to each frame of point cloud data, and a target detection labeling result is determined. By adopting the method, the image annotation efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image labeling method, device, computer equipment, storage medium and computer program product. Background technique [0002] 3D visual target detection is a very important technical core in autonomous driving scenarios, and the main means of 3D visual target detection relies on marking the collected visual images, and detecting the target objects in the visual images in the form of target detection frames. Make an annotation. [0003] The current image annotation method generally adopts the method of manual annotation. The collected point cloud data is manually annotated through the annotation tool to generate the target detection frame corresponding to the target object, and then the target detection frame is manually adjusted based on the image data. [0004] However, labeling hundreds of thousands of frames of images requires a lot of human resources an...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/207G06T7/80G06N3/04G06N3/08G06V10/46
Inventor 童柏琛赵航任炳魁陈贺李文俊倪凯
Owner 禾多科技(北京)有限公司
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