Lane recognition method based on full convolutional neural network

A convolutional neural network and lane technology, applied in the field of lane recognition with deep learning methods, can solve problems such as performance degradation

Inactive Publication Date: 2019-08-23
CHONGQING UNIV OF POSTS & TELECOMM
View PDF9 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Moreover, these methods can exhibit performan...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Lane recognition method based on full convolutional neural network
  • Lane recognition method based on full convolutional neural network
  • Lane recognition method based on full convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The implementation of the present invention will be described in detail below.

[0044] The present invention is a lane recognition method of a fully convolutional neural network. In an embodiment, a fully convolutional neural network is constructed to identify lanes. The specific implementation of this method is as follows:

[0045] 1. The data set labels used in this paper are a set of perspective images, so the final output data of the deep learning network constructed in this design will also be drawn into a set of perspective images. The image segmentation model is essentially a classification network, but its classification goal is at the pixel level, and the pixels of the entire image are classified sequentially. For lane line detection, it is to determine whether the pixels in the image belong to the background or the lane line. Marking the pixels determined as lane lines to form a region is to complete the lane line detection based on image segmentation. For...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

In order to solve the problem of lane recognition in current vehicle automatic driving, a neural network model is planned to recognize a lane as a final solution. Compared with a typical computer vision technology in which a gradient threshold value, a color threshold value and other manual programming methods are used for processing pictures, the deep learning model can achieve a better effect inthe aspect of extracting key characteristic values of the images. In particular, a full convolutional neural network model, namely a neural network without any full connection layer is constructed; important specific pixel groups are found by using a filter, lane feature information in an image can be extracted very effectively, and finally output information can be displayed in a lane area recognized by an original image in a highlight manner, so that the purpose of lane recognition is achieved.

Description

technical field [0001] This patent belongs to the field of automobile automatic driving perception technology, in particular, it involves the scheme of identifying lanes with deep learning methods. Background technique [0002] Lane detection remains a fertile area of ​​machine vision research. Therefore, many methods have been proposed in the world to accomplish this task. However, the Hough transform method is still one of the most commonly used methods. In these methods, the input image is first preprocessed using a Canny edge detector or a steerable filter to find edges. The classic Hough transform is used to find straight lines in binary images, which usually correspond to lane boundaries. But with the Hough transform, it is often difficult to determine whether a line corresponds to a lane boundary. In color segmentation methods, RGB images are often converted to HSI or custom color spaces. In these alternative color spaces, the luma and chrominance components of a...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/588G06F18/214
Inventor 朴昌浩雷震鲁冲
Owner CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap