Tunnel Lane Recognition Optimization Method Based on Deep Learning
This paper proposes an optimization method for tunnel lane recognition based on deep learning,and constructs a self-made data set for experimental verification.First,a tunnel lane recognition framework based on deep learning was studied.Secondly,in view of the shortcomings of the framework,data preprocessing was proposed to enhance data learnability,and the MobileNetV3 model was introduced to effectively capture the complex lane structure in the tunnel;Finally,experimental verification was carried out through a self-made data set.Experimental results show that in a tunnel environment,this method can accurately identify lanes and has good performance.
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