Lane line detection method, electronic equipment and vehicle

Abstract

The invention provides a lane line detection method, electronic equipment and a vehicle, and the method comprises the steps: inputting a rasterized vehicle foresight picture into a pre-trained model based on deep learning, converting a lane line instance segmentation problem into two sub-tasks: category perception prediction and segmentation mask prediction of grid units, and if a lane line falls in some grid units, the grid units are responsible for predicting classification information and segmentation information of the lane line at the same time; and the classification information and the segmentation information output by the model are simply processed to directly output an instance segmentation result of each lane line due to the fact that the grid units in the picture correspond to lane line instances; and therefore, the complex post-processing step is omitted, a large number of computing resources are saved, and the method can be deployed on low-cost vehicle-mounted embedded equipment on a large scale.

Description

technical field [0001] The invention belongs to the technical field of lane line detection, and in particular relates to a lane line detection method, electronic equipment and a vehicle. Background technique [0002] At this stage, with the development and maturity of artificial intelligence technology, autonomous driving technology and advanced assisted driving systems have become popular research fields. Lane line detection is one of the basic and important tasks. Its goal is to efficiently and accurately detect the position of the lane line on the road, so as to ensure that the vehicle is positioned in the current driving lane and assist subsequent lane departure or trajectory. The planning module makes sound decisions. Most existing vehicles are equipped with front-view cameras. We can obtain the road image in front of the vehicle in real time through the vehicle vision system, so as to identify and locate the lane line on the image to calculate the position of each lan...

AI Technical Summary

Problems solved by technology

[0004] There are still some problems in the practical application of the above method. The lane line feature vector branch also needs complex and time-consuming post-processing steps, and relies on manually formulated lane line inference rules, which is difficult to carry out large-scale on-board low-cost embedded devices. deploy

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