基于模型融合的低照度环境下车道线检测方法

针对低照度环境下车道线检测准确率低和稳定性差的问题，提出了一种基于模型融合的低照度车道线检测算法.采用基于ALTM(adaptive local tone mapping)算法改进的颜色平衡算法做数据增强处理，有利于车道线特征的提取;融合改进的Deeplabv3+模型和Unet模型，有效降低了过拟合现象;使用实例分割得到分割后的车道线图像.实验证明，改进的Unet模型和Deeplabv3+模型的mean_IOU(mean intersection-over-union)值分别达到了0.625，0.646，较原始模型分别提高了2%和4.6%，最终融合结果提升了0.01%.提升了低照度环境下车道线检测的稳定性和准确性.

Method of Lane Line Detection in Low Illumination Environment Based on Model Fusion

Aiming at the problem of low accuracy and poor stability of lane line detection in low illumination environment， an algorithm of lane line detection in low illumination environment based on model fusion was proposed. The improved color balance algorithm based on ALTM(adaptive local tone mapping) algorithm is adopted for data enhancement processing， which is beneficial for the extraction of lane line features. The improved Deeplabv3+model and Unet model are fused to reduce the overfitting. The segmented lane line image is obtained by instance segmentation. The experimental results show that the mean_IOU(mean intersection-over-union) values of the improved Unet model and Deeplabv3+model reach 0.625 and 0.646， respectively， which are 2% and 4.6% higher than the original model. The final fusion result increased by 0.01%. The stability and accuracy of lane line detection are promoted in low illumination environment.