基于实例分割的复杂环境车道线检测方法

针对基于语义分割的车道线检测方法存在的特征表述模糊、语义信息利用率较低的问题，采用实例分割算法，提出基于改进混合任务级联（HTC）网络的车道线检测方法. 基于HTC网络模型，在主干网络中引入可变形卷积，提升主干网络对复杂环境中车道线特征的提取能力. 改进特征金字塔网络结构，在特征金字塔网络的基础上添加自底向上的低层特征传递路径，引入空洞卷积，在不损失车道线特征信息的情况下增加特征图感受野，利用低层特征中所包含的车道线的精确定位信息，提高车道线的检测精度. 实验结果表明，改进HTC网络模型可以实现车道线特征的鲁棒提取，在复杂道路环境中可以获得较好的检测性能，有效提高了车道线检测精度.

Lane detection method in complex environments based on instance segmentation

An instance segmentation algorithm was adopted and a lane detection method based on improved hybrid task cascade (HTC) network was proposed aiming at the problems of fuzzy feature representation and low semantic information utilization in the lane detection method based on semantic segmentation. Deformable convolution was introduced into the backbone network based on HTC network model in order to improve the ability of the backbone network to extract lane features in complex environments. The structure of feature pyramid network was improved by adding a bottom-up low-level feature transmission path based on feature pyramid network, and dilated convolution was introduced to increase the receptive field of feature map without loss of lane feature information. The accurate location information of lane lines contained in the low-level features was used to improve the accuracy of lane detection. The experimental results show that the improved HTC network model can realize the robust extraction of lane features, obtain better detection performance in complex road environments, and effectively improve the accuracy of lane detection.