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A Checkerboard Checking Method Based on Deep Learning Semantic Segmentation

A semantic segmentation and deep learning technology, applied in the field of automotive driving assistance, can solve the problems of easy false detection, lack of verification information, difficult to eliminate, etc., and achieve the effect of high precision

Active Publication Date: 2022-05-27
SAIC MAXUS AUTOMOTIVE CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But because it is the corner point of the checkerboard that is directly detected, there is no other verification information. When the corner point given by deep learning is wrong, it is difficult to rule it out.
At the same time, the checkerboard corner feature is easily disturbed by other objects in the physical world. Sometimes the corner of a stain on the ground may also be considered as a checkerboard corner point, so this method is prone to false detection.

Method used

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  • A Checkerboard Checking Method Based on Deep Learning Semantic Segmentation
  • A Checkerboard Checking Method Based on Deep Learning Semantic Segmentation
  • A Checkerboard Checking Method Based on Deep Learning Semantic Segmentation

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] The invention is a checkerboard detection algorithm with strong adaptability to the environment and high robustness. The scheme is designed by combining the relatively strong adaptability of deep learning to the environment and the high robustness of OCamCalib in checkerboard inspection.

[0033] The implementation of this scheme is mainly divided into the following steps:

[0034] Use deep learning semantic segmentation to segment checkerboards from images. Implementing this step involves choosing a segmentation network, GT (groudture) annotation, network parameter training, and final prediction.

[0035] The segmentation network can choose the commonly used DeepLab, etc., or multiple resnet and other networks can be modified to realize the segmentation function. It should be noted that some target checkerboards only occupy 6*6 pixels in the image, so the seg...

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Abstract

The present invention relates to a checkerboard inspection method based on deep learning semantic segmentation. The method is used for detecting checkerboard when any camera is calibrated. Firstly, a checkerboard image is obtained to achieve the purpose of calibrating the internal and external parameters of the camera. Take an image and checkerboard it. Compared with traditional image processing methods, this application has high accuracy and small error, and can combine the advantages of traditional checkerboard detection to improve the adaptability to interference items in the environment, and also improve the adaptability to ambient light.

Description

technical field [0001] The invention relates to a checkerboard checking method based on deep learning semantic segmentation, and belongs to the technical field of automobile driving assistance. Background technique [0002] At present, camera-based vision systems are playing an increasingly important role in intelligent driving vehicles. Cameras are similar to the eyes of a smart driving car and are used for object detection to judge the surrounding environment. After detecting the target, it is necessary to know the distance from the target to the car, which can be calculated by the pose of the camera relative to the car. The pose of the camera relative to the car needs to be obtained through calibration. [0003] Camera calibration mainly refers to calculating the internal and external parameters of the camera (relative to the pose of the car). At present, the most commonly used camera calibration is to calculate the internal and external parameters of the camera by det...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/80G06N3/04G06N3/08
CPCG06T7/80G06N3/08G06T2207/20081G06T2207/20084G06N3/045
Inventor 杨盼王瑞罗先伟朱晶星赖杰
Owner SAIC MAXUS AUTOMOTIVE CO LTD