DRSTNet:用于车道检测的扩展残差卷积鲁棒时空网络

Jiyong Zhang, T. Deng, Fei Yan, Wenbo Liu
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引用次数: 0

摘要

车道检测在保证自动驾驶安全、车道偏离预警等方面发挥着越来越重要的作用。虽然在车道检测方面已经进行了大量的创新研究,但在具有挑战性的场景下追求车道检测的高准确性仍然是一个开放的研究问题。在这项工作中,我们提出了一个通过扩展残差卷积和时空网络(DRSTNet)的鲁棒车道检测模型。扩展残差卷积使我们的模型能够通过扩展卷积的接受域获得更丰富、更密集的特征信息,并通过跳过连接为我们的模型提供必要的补充。此外,时空网络通过卷积门控循环单元(convgru)处理时空信息,进一步增强了模型提取有效特征的学习能力。此外,大量的实验验证了我们的模型在提高鲁棒性和减小权重参数大小的同时优于最先进的算法,在DET上达到81.35%,在CULane上达到73.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DRSTNet: A Robust Spatio-temporal Network with Dilated Residual Convolutions for Lane Detection
Lane detection plays a more and more significant role in ensuring the safety of autonomous driving, Lane Departure Warning, etc. Although a lot of research has been conducted with innovative methods on lane detection, pursuing the high accuracy of lane detection in challenging scenarios is still an open research question. In this work, we present a robust lane detection model via dilated residual convolutions and spatio-temporal networks (DRSTNet). The dilated residual convolutions make our model have the ability to obtain richer and denser feature information by expanding the receptive fields of the convolutions, and provide our model with necessary supplements by skip connections. In addition, the spatio-temporal networks further enhance the learning ability of our model in extracting effective features by dealing with spatial and temporal information via convolutional gated recurrent units (ConvGRUs). Furthermore, a large number of experiments verify that our model outperforms the state-of-the-art algorithms while increasing the robustness and reducing the size of the weight parameter, achieving 81.35% on DET and 73.0% on CULane.
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