Lane line detection system and method combining double-branch network and user-defined function network

A lane line detection and branch network technology, applied in the field of unmanned driving, can solve the problem of low detection accuracy

Inactive Publication Date: 2019-09-03
DALIAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing lane line detection technology, the present invention provides a lane line detection method combined with a double-branch network and a self-defined function network, which can

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  • Lane line detection system and method combining double-branch network and user-defined function network
  • Lane line detection system and method combining double-branch network and user-defined function network
  • Lane line detection system and method combining double-branch network and user-defined function network

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Embodiment

[0048] Embodiment: Lane line detection system and method combined with double-branch network and self-defined function network

[0049] This embodiment provides a lane line detection system combined with a double-branch network and a self-defined function network, which includes an image input module, a lane instance segmentation branch module, a lane instance embedding branch module, and a lane line fitting module.

[0050] Among them, the image input module is connected with the lane instance segmentation branch module, the lane instance embedding branch module and the lane line fitting module. The image input module is used to input the road lane image, set the size of the input road lane image, and set the input parameters of the double-branch network and the self-defined function network.

[0051] The lane instance segmentation branch module is connected with the lane instance embedding branch module and the lane line fitting module. The lane instance segmentation branch ...

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Abstract

The invention discloses a lane line detection system and method combining a double-branch network and a user-defined function network. The method comprises the steps of improving a classic convolutional neural network structure, reconfiguring a coder-decoder composition structure, and forming a double-branch network detection lane line; and training a convolutional network with a self-defined lossfunction, allowing the transformation matrix parameters to be adjusted when the road gradient changes, and constructing a self-defined function network to fit the lane pixels. According to the method, the double-branch network comprises a lane segmentation branch and a lane embedding branch, wherein the segmentation branch divides an image into a background part and a lane part, the lane embedding branch divides the lane pixels into different examples, and the method for detecting fixed lane lines is improved. According to the method, the user-defined function network generates the convertible matrix fitting lane pixels, so that the problem of poor fitting of distant lane pixels is solved. According to the present invention, the lane line is detected by adopting the double-branch networkand the user-defined function network, so that the technical problem of low detection accuracy under complex road conditions is solved.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving, and specifically relates to a lane line detection system and method combined with a double-branch network and a self-defined function network, which is suitable for detection of vehicle lane changes and changes in road plane slopes. Background technique [0002] As an important link in the field of unmanned driving and an important part of intelligent assisted driving, lane line detection plays an important role in reducing road traffic accidents, and has been highly valued by researchers and engineering practitioners. [0003] Lane line detection is defined as follows: by detecting the input image, then using a series of feature extraction algorithms to extract the lane line pixel features, using high-order polynomial fitting to extract pixels from the lane, and finally displaying in the original input image and outputting the image. The purpose of lane line detection is to locate the e...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06T7/194G06F17/16
CPCG06T7/194G06F17/16G06V20/588G06N3/045
Inventor 高俊杰陈乙庆赵鹏崔晓敏王强韩贤贤林家乐侯宛伶
Owner DALIAN UNIV OF TECH
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