Target detection box generation method and image data automatic labeling method and system

Abstract

The invention provides a target detection box generation method and an image data automatic labeling method and system. The generation method comprises the following steps: acquiring a real-time image with a target object, a world coordinate of a center point of the target object under a world coordinate system, a world coordinate of an image collector and attitude information of the image collector; according to the world coordinate of the central point of the target object in the world coordinate system, the world coordinate of the image collector and the attitude information of the image collector, obtaining the pixel coordinate of the central point of the target object through coordinate transformation from the world coordinate system to a pixel coordinate system; and taking the pixel coordinate of the central point of the target object as the central point of a target detection box, and in combination with the calibrated side length or diameter of the target detection frame, generating the target detection box. According to the method, on the premise that human intervention is not needed, automatic generation of the target detection data is truly realized, and the operation process is simplified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for generating a target detection frame, an image data automatic labeling method and a system. Background technique [0002] In the existing technology, supervised learning requires a large amount of labeled data, which can be completely labeled manually, automatically labeled by a machine, or a mixture of humans and machines. These three modes are called manual labeling, automatic labeling and semi-automatic labeling. For example, in automatic driving, the perception system has a great demand for labeling, especially obstacle perception. Its automatic labeling can mainly use the mutual labeling between different sensors. There are three mainstream sensors for obstacle perception: LiDAR, Camera, and Radar. From the perspective of the data flow of automatic labeling, it is generally to rely on lidar and millimeter-wave radar to label the camera. Th...

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Problems solved by technology

[0005] In order to overcome the above-mentioned defects of the prior art, the object of the present invention is to provide a method for generating a target detection frame, a method and system for automatically labeling image data, and the method and system are used to solve the problem of human intervention in the prior art, cumbersome process and time consuming problem

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