Simulation of Lane Detection Algorithm in Low-Light Environment Based on Deep Learning
Lane line detection research is the basis to ensure the safety of vehicle automatic driving,but there are some problems in the current research,such as poor stability and low accuracy of lane line detection in low-light envi-ronment,so this paper proposes a deep learning algoritm based on improved BFA-Retinex illumination compensation to improve the illumination level of key areas of the image.The BRI-SVM low-light lane line detection and recogni-tion model was constructed,which includes three modules:image light filling module,feature fusion module and lane line detection module.Firstly,the Lll low-light lane data set was grayed and standardized by the BRI-SVM model,and then the improved bilateral filtering algorithm was used to improve the image illumination benchmark,and then the H and G features in the optimized image were extracted and organically fused;Finally,based on the data-driven method,the BRI-SVM model was constructed with the deep learning and SVM algorithm as the core,and the perform-ance of the model was improved by cross-validation.The simulation results of the multi-class fusion algorithm model show that,on the Lll low-light data set,compared with other models,the BRI-SVM model has the highest stability and comprehensive performance,with the eigenvalues reaching 96.1%and 96.3%,respectively,and an average in-crease of 24.4%and 23.8%,respectively,compared with the traditional algorithm;In addition,the model constructed in this paper has good detection timeliness and detection accuracy,ranking second in all model evaluations.To sum up,the lane detection model based on the improved BFA-Retinex algorithm in low-light environment has the highest robustness and stability,and greatly improves the accuracy and timeliness of lane detection.
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