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Abstract
Lane Line Detection Based on Improved Semantic Segmentation in Complex Road Environment
Highlights
In recent years, autonomous driving technology has become a research hotspot in the field of intelligent transportation systems and has attracted considerable attention
To solve the problem of low lane line detection accuracy of the existing methods in complex road scenes, in this paper, we propose an end-to-end semantic segmentation network model based on the Visual Geometry Group–Spatial Convolutional Neural Network (VGG-SS), which represents an optimized VGG16 network and improves the lane line detection accuracy by embedding a self-attentive distillation model between the encoder and the decoder, and a spatial convolution neural network (SCNN) model in the top implicit layer
We propose a lane line detection method based on improved semantic segmentation, which solves the problem of low detection accuracy because of damaged and obscured lane lines in complex road scenes
Summary
Autonomous driving technology has become a research hotspot in the field of intelligent transportation systems and has attracted considerable attention. Kumar et al used the Kalman-filter-based tracking method to detect lane lines to solve the problem of low robustness of detection algorithms in illuminated scenes.[13] Chi et al used the road vanishing point estimation method to detect lane lines, but their model-based method is computationally expensive.[14] In addition, the model-based detection method is computationally intensive and can perform well only in specific environments, which poses certain limitations Both feature- and model-based traditional lane line detection methods are susceptible to external environmental factors, and their robustness is extremely low when the lane lines are broken, obscured, or unpainted,(15,16) which can result in incorrect or even impossible lane line detection. We propose a lane line detection method based on improved semantic segmentation, which solves the problem of low detection accuracy because of damaged and obscured lane lines in complex road scenes
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