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

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F1(IoU=0.5)评分为69.3%,达到了速度与精度的良好平衡。

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

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 is 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 F1 score on the CULane dataset (IoU=0.5) is 69.3%, achieving a good balance between speed and accuracy.