循环多特征信息融合法:一种基于深度学习的车道线检测方法

Recurrent Multi-feature Information Aggregation:A Deep Learning-Based Lane Detection Approach

车道线检测是辅助驾驶和自动驾驶的核心技术之一。为了进一步增强车道线特征的提取能力,提出了一种基于深度学习的循环多特征信息融合车道线识别算法。针对模型计算效率问题,该算法将车道线检测问题视为基于行选择单元格的分类问题;针对图像中车道信息聚合问题,提出了一种新的循环多特征信息聚合(recurrent multi-feature information aggregator,RMFA)方法,并将该方法与残差神经网络(residual neural network,ResNet)相结合提出融合上下文及多通道信息的车道线识别网络ResNet-RMFA。将该网络模型在Tusimple和CULane公开数据集上进行了性能测试,实验结果表明该模型单帧图像的推理时间可达4.8 ms,在Tusimple数据集上的精确度为96.07%,在CULane数据集上的F_(1)(IoU=0.5)评分为69.3%,达到了速度与精度的良好平衡。

Lane detection is one of the core technologies of assisted driving and automatic driving.To further enhance the ability of lane feature extraction,a lane line recognition algorithm based on deep learning and recurrent multi-feature information aggregator was proposed.Given the problem of model operation speed,the algorithm took the lane detection problem as a classification problem based on row selection cells.Given the lane information aggregation problem in the image,a novel RMFA(recurrent multi-feature information aggregator)method was proposed.The ResNet(residual neural network)was combined with the method to propose a lane line recognition network ResNet-RMFA fused with contextual and multi-channel information.The performance of the network model was tested on the open datasets of Tusimple and CULane.The experimental results show that the reasoning time of a single frame image of the model can reach 4.8 ms,the accuracy on the Tusimple dataset is 96.07%,and the F_(1)score on the CULane dataset(IoU=0.5)is 69.3%,achieving a good balance between speed and accuracy.