论文标题
基于端到端神经网络的复杂场景中的车道检测
Lane detection in complex scenes based on end-to-end neural network
论文作者
论文摘要
车道检测是解决无人驾驶中可衍生区域的划分的关键问题，而车道线的检测准确性在车辆驾驶决策中起着重要作用。每天驾驶中车辆面临的场景相对复杂。明亮的光线，光线不足和拥挤的车辆将带来不同程度的难度探测。因此，我们结合了空间卷积在空间信息处理中的优势和语义分割中ERFNET的效率，提出了一个端到端网络，以在各种复杂场景中进行车道检测。我们通过将空间卷积和扩张卷积结合起来设计信息交换块，这在理解详细信息中发挥了重要作用。最后，我们的网络在Culane数据库上进行了测试，其F1量度为0.5的F1量度可以达到71.9％。
The lane detection is a key problem to solve the division of derivable areas in unmanned driving, and the detection accuracy of lane lines plays an important role in the decision-making of vehicle driving. Scenes faced by vehicles in daily driving are relatively complex. Bright light, insufficient light, and crowded vehicles will bring varying degrees of difficulty to lane detection. So we combine the advantages of spatial convolution in spatial information processing and the efficiency of ERFNet in semantic segmentation, propose an end-to-end network to lane detection in a variety of complex scenes. And we design the information exchange block by combining spatial convolution and dilated convolution, which plays a great role in understanding detailed information. Finally, our network was tested on the CULane database and its F1-measure with IOU threshold of 0.5 can reach 71.9%.
