Lane line dynamic detection and lane boundary fitting method

Abstract

The invention discloses a lane line dynamic detection and lane boundary fitting method, which comprises the following steps of: performing lane line detection from an image frame of a road aerial video, marking a lane line, and fitting a cubic parabolic equation of the lane line. The method comprises three parts of extracting aerial road video frames, training a generative adversarial network, detecting lane lines and fitting a lane line equation. The method comprises the following steps: firstly, extracting an image frame and manually marking a lane line with a special color; training a deepnetwork based on conditional adversarial, and detecting lane lines in the test set; and finally, extracting coordinates of special color pixel points on the lane line, and fitting by using a least square method to obtain a cubic parabolic equation of the lane line. The method is applied to lane line detection and fitting in an aerial road video, the detection performance of the method is verified,the result shows that the method has high accuracy for lane lines at different positions, and a good detection effect is obtained.

Description

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AI Technical Summary

Benefits of technology

In this patented technology, it suggests that there are methods called Dynamic Detection (DD) or Layer Projection Network(LPN), which can accurately identify lanestrians along roads without relying solely upon visual inspection by human operators. These techniques help prevent accidents caused by vehicles driving over these areas while avoiding collisions between pedestrian traffic and other objects such as cars.

Problems solved by technology

Layered line detection (LLID) involves analyzing data from multiple sensors placed at different locations along a road's surface or pavement markings during construction work hours. It uses advanced techniques like laser radars that can accurately determine if there were any signs of traffic congestion caused by vehicles driving over them without interferring too much detail about their surroundings. However, current systems have limitations due to factors including environmental conditions, sensor performance issues, and human error.

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