Research on lane detection algorithm based on deep learning
In recent years,with the rapid upgrading of software and hardware devices,intelligent devices have developed rapidly.Intelligent assisted driving technology is gradually being implemented.One of the key issues that intelligent assisted driving needs to solve is the contradiction between lane lines,vehicles,and pedestrians.The following research has been conducted on lane line detection in intelligent assisted driving systems.For the lane detection algorithm,an irregular encoder-decoder network structure and the idea of multi-task learning were adopted to improve the lane detection algorithm LaneNet and optimize the convolution module,solving the problems of difficult detection in complex scenes and low model accuracy.The method used in the article has been trained on a large amount of data and validated on actual datasets.The accuracy of lane detection on the Tusimple dataset is 96.42%,with a parameter size of 5.14 M.The test results indicate that lane detection has a good effect and meets the research objectives of this article.
lane detectionassisted intelligent drivingdeep learning
NETL
NSTL
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