Lane line detection method based on deep learning

Abstract

The invention discloses a lane line detection method based on deep learning. The method comprises the following steps: S01, obtaining a lane detection data set, wherein the lane detection data set comprises a plurality of images containing lanes; S02, building a training model, and setting a loss function and constraint parameters, wherein the training model comprises a convolution layer, a pooling layer, a residual block and a full connection layer which are connected in sequence, the loss function is used for limiting the smoothness and rigidity of the lane line; S03, training the training model by adopting the lane detection data set, and iterating for multiple times to obtain a convergent detection model; and S04, placing the detection model in a vehicle-mounted camera to obtain a lanedetection result. According to the lane line detection method based on deep learning provided by the invention, the lane line can be rapidly detected while the accuracy is ensured.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a lane line detection method based on deep learning. Background technique [0002] With the development of computer hardware technology and computer vision technology, unmanned driving based on computer vision becomes possible, and lane line detection, as an important part of unmanned driving system, needs to keep running in real time during driving. Moreover, multiple cameras are often loaded in a vehicle, which requires a simple and efficient algorithm for lane line detection. [0003] The traditional method is to regard lane line detection as a segmentation problem, classify all the pixels in the image, and then find the lane line according to the classification results; the steps and operations of segmentation and classification lead to a very complex and slow model. At the same time, because the segmentation does not utilize global information, the detection effect is...

AI Technical Summary

Problems solved by technology

[0003] The traditional method is to regard lane line detection as a segmentation problem, classify all the pixels in the image, and then find the lane line according to the classification results; the steps and operations of segmentation and classification make the model very complex and slow

At the same time, because the segmentation does not utilize global information, the detection effect is not ideal when the lane line is blocked or the light condition is poor.

Another problem brought about by segmentation is the receptive field problem. Because segmentation is generally obtained by full convolution, the receptive field of each pixel is limited, and the lane line cannot be accurately and quickly judged based on limited features.

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