计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 416-419.doi: 10.11896/jsjkx.201100206
陈浩楠, 雷印杰, 王浩
CHEN Hao-nan, LEI Yin-jie, WANG Hao
摘要： 随着深度学习的发展,基于深度卷积神经网络的车道线检测模型在自动驾驶系统和高级辅助驾驶系统中得到了广泛的应用。这些模型虽然有较高的精度,但通常计算量大且运行速度慢。为了解决该问题,提出了一种车道线检测任务专用的轻量神经网络模型。首先,提出了一种行列解耦采样的卷积模块,该模块利用图像中车道线区域的行列可分解性对传统的残差卷积模块进行了合理的优化。其次,利用深度可分离卷积技术进一步降低行列解耦采样卷积模块的计算量。此外,还设计了一种金字塔空洞卷积模块来增加模型的感受野。在CULane数据集上的实验的结果表明,文中提出的轻量车道线检测模型与之前最好的SCNN模型相比,浮点计算量降低了95.2%,F1分数提高了1.0%,在保持较高精度的前提下显著降低了车道线检测模型的计算量。
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