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A lane line detection method based on a deep segmentation network

A technology of lane line detection and depth segmentation, which is applied in biological neural network models, instruments, character and pattern recognition, etc., can solve problems such as changes, and achieve improved robustness, efficient lane line detection algorithms, good robustness and real-time effect

Active Publication Date: 2019-04-16
安徽科大擎天科技有限公司
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Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a lane line detection method based on a deep segmentation network, in order to effectively solve the problem of lane line changes, thereby being suitable for lane line detection under various complex road conditions, And improve the robustness and real-time performance of detection

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  • A lane line detection method based on a deep segmentation network
  • A lane line detection method based on a deep segmentation network
  • A lane line detection method based on a deep segmentation network

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Embodiment Construction

[0029] In this example, if figure 1 As shown, a lane line detection method based on deep segmentation network is carried out as follows:

[0030] Step 1. Obtain the original image set with the label of each lane line from the Tucson database; use the python language and use the third-party library opencv to set the corresponding gray value for the lane line label on any i-th original image ( The gray value of the first lane line label is set to 220, the gray value of the second lane line is reduced by 50, and so on), and the background gray value of the i-th original image is set to zero, thus obtaining the i-th A grayscale image of a lane line example, for example image 3 As shown, the i-th original image and the i-th lane line instance segmentation grayscale image are normalized to 512×256 to obtain the i-th original image and the i-th lane line instance segmentation after normalization Grayscale image; thereby obtaining the normalized original image set and the grayscale...

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Abstract

The invention discloses a lane line detection method based on a deep segmentation network. The method comprises the steps of 1, obtaining a normalized original image set and a lane line instance segmentation gray level image set; 2, constructing a multi-layer depth segmentation network, and training to obtain an optimal multi-layer depth segmentation network; 3, obtaining a lane line binary imageand a background binary image; 4, obtaining a feature map of the to-be-predicted road image; 5, obtaining a feature map of the to-be-predicted road image; 6, obtaining a lane line instance segmented image; And 7, obtaining a detection result map of the lane line. The method can effectively solve the problem of lane line change, can be suitable for lane line detection under various complex road conditions, and improves the robustness and real-time performance of detection.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving, and in particular relates to a lane line detection method based on a deep segmentation network. Background technique [0002] Modern smart cars incorporate many car-assisted driving functions, including Lane Departure Warning System (Lane Departure Warning System, LDWS) and Lane Keeping Assist System (Lane Keeping Assist System, LKAS), which enable the car to drive in the correct lane. Lane line detection is a key technology in lane departure warning system and lane keeping assist system. However, due to the complexity of road scenes and high real-time requirements for lane line detection, lane line detection is still unmanned. A difficult problem in the field of driving technology. [0003] At present, lane line detection methods can be roughly divided into feature-based detection methods, model-based detection methods and deep learning-based detection methods. The method based on han...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/588G06N3/045
Inventor 孙锐丁海涛阚俊松吴柳玮
Owner 安徽科大擎天科技有限公司
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