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Lane line detection method based on cross-layer optimization

A technology for lane line detection and cross-layer optimization, applied in the field of computer vision, can solve the problem of low detection accuracy, and achieve the effect of improving detection accuracy

Active Publication Date: 2022-05-06
HANGZHOU FABU TECH CO LTD
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Problems solved by technology

These methods do not combine high-level and low-level features, so the detection accuracy is not high

Method used

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  • Lane line detection method based on cross-layer optimization
  • Lane line detection method based on cross-layer optimization
  • Lane line detection method based on cross-layer optimization

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Embodiment Construction

[0033] The present invention will be further elaborated and illustrated below in conjunction with the accompanying drawings and specific embodiments.

[0034] Such as figure 1 As shown, the embodiments of the present invention include the following:

[0035] (1) Input the road picture and use the convolutional neural network to extract the pyramid level feature map in the road picture;

[0036] In step (1), the convolutional neural network includes multiple convolution modules. The input road picture is processed by multiple convolution modules in succession, and then the results processed by adjacent different convolution modules are transferred and superimposed to obtain multiple pyramids. Hierarchical feature maps.

[0037] Specifically, the convolutional neural network processes the input road picture through three consecutive convolution modules, and obtains a backbone feature map after each convolution module processing, and obtains high-level and middle-level images ...

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Abstract

The invention discloses a lane line detection method based on cross-layer optimization. Inputting a road picture and obtaining a pyramid hierarchical feature map by using a convolutional neural network; using a high level to preliminarily detect and position lane line parameters through a detector, and using the detected and positioned lane line parameters as low level input for continuous detection; continuously repeating the steps to obtain a final predicted lane line; training a lane line detection model, repeating the steps, and optimizing by using a loss function until convergence; and inputting a real-time to-be-detected road picture into the trained lane line detection model to obtain a lane line position in the road picture. The lane line is detected by combining high-level and low-level network features, the detection precision is improved, and the method has superiority.

Description

technical field [0001] The invention relates to a lane line image processing method in the field of computer vision, in particular to a lane line detection method based on cross-layer optimization. Background technique [0002] Lane line detection is an important task in the field of computer vision. It is a field of mutual promotion and development with deep learning. It can be applied to automatic driving or assisted driving to provide it with road lane line information, thereby helping intelligent vehicles to better locate the vehicle position. [0003] Lane line detection is a very challenging task in computer vision. A lane line is a traffic sign with very high-level semantic information. The shape of lane lines is similar to some road signs, but they have different semantic information. High-level features are very important for lane line detection. But the appearance of lane lines is simple and requires low-level local features for accurate localization. Therefor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/58G06V10/25G06V10/40G06V10/82G06N3/04G06N3/08
CPCG06N3/08G06N3/045Y02T10/40
Inventor 郑途黄亦非刘洋唐文剑杨政何晓飞
Owner HANGZHOU FABU TECH CO LTD
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