Front small-region object recognition and vehicle early warning method

A technology for object recognition and small areas, applied in scene recognition, vehicle parts, vehicle safety arrangements, etc., can solve the problems of low accuracy of distance recognition and poor real-time performance

Inactive Publication Date: 2018-03-30
JILIN UNIV
View PDF8 Cites 78 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the uncertainty of the size and distance of objects in a small area in the image and the problems of low recognition accuracy and poor real-time performance caused by the use of traditional target detect...

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
  • Front small-region object recognition and vehicle early warning method
  • Front small-region object recognition and vehicle early warning method
  • Front small-region object recognition and vehicle early warning method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0074] Below in conjunction with the accompanying drawings, the present invention is further described: the present invention proposes a method for object recognition and vehicle early warning in a small area ahead. First, the projection matrix of world coordinates and image pixel coordinates is obtained, and a relationship between the millimeter wave radar coordinate system and the image pixel coordinate system is established. Then, the distance, relative speed and target number ID of the target in front of the vehicle, that is, the obstacle, are obtained through the millimeter-wave radar. According to the distance information, it is judged whether the object in the small area is a small object at a short distance, and the long-distance target is excluded. Determine After the effective target, the millimeter-wave radar scanning point in the millimeter-wave radar coordinate system is projected to the image pixel coordinate system, and the area of ​​interest ROI is established on...

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

The invention relates to a front small-region object recognition and vehicle early warning method, and belongs to technical field of early warning for collision in front of vehicles. The front small-region object recognition and vehicle early warning method specifically comprises the steps that an image of a target to be recognized in front of a vehicle is acquired, and a region of interest (ROI)image is obtained; image preprocessing is performed on the obtained region of interest image, the preprocessed region of interest image is inputted into an improved YOLO convolutional network structure model to perform target recognition, and a target regression frame and a target category are outputted; the recognition result is inputted into a vehicle early warning system, if the target regression frame is located beyond the range of a vehicle driving route or the target is recognized to be of a non-dangerous category, the target is marked as a non-dangerous target, and the vehicle does notperform early warning; and if the target regression frame is located within the vehicle driving route and the recognized object belongs to a dangerous category or the object cannot be recognized but has certain speed features, the target is marked as a dangerous target, the vehicle performs early warning to remind a driver to pay attention so as to avoid occurrence of an accident.

Description

technical field [0001] The invention belongs to the technical field of vehicle front collision warning, in particular to a method for identifying objects in a small area ahead and a vehicle warning method based on an improved YOLO convolutional network structure model. Background technique [0002] With the expansion of the metropolitan area and the development of science and technology, the advanced driving assistance system for automobiles has been developed rapidly. Among them, the forward collision warning system has played an active role in improving road traffic safety, and has become a hot spot in the current international intelligent transportation system research. It can effectively detect and identify the objects ahead in real time, and send possible danger signals to the vehicle early warning system, which can effectively prevent the occurrence of vehicle collision accidents. [0003] Nowadays, there are a variety of target detection methods at home and abroad: th...

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): G06K9/00G06K9/32G06K9/62B60R21/013B60Q9/00
CPCB60Q9/008B60R21/013G06V20/58G06V10/25G06V2201/07G06F18/214G06F18/24
Inventor 金立生朱菲婷夏海鹏冯成浩朱佳超高铭陈梅郭柏苍闫福刚司法
Owner JILIN UNIV
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