LaneSegNet:一种高效的车道线检测方法
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作者单位:

1.淮阴工学院;2.昆明理工大学

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基金项目:

国家重点研发计划


Lanesegnet: an efficient lane line detection method
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Affiliation:

1.Huaiyin Institute of Technology;2.Kunming University of Science and Technology

Fund Project:

National Key Research and Development Program of China

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    摘要:

    车道线检测在智能交通领域占有重要地位,其检测的准确度和速度对于辅助驾驶以及自动驾驶有重要影响。针对目前深度学习方法识别车道线精度差、速度慢的问题,提出了一种高效的车道线分割方法LaneSegNet。首先基于编码和解码网络原理构建主干网络Lane-Net,用于提取车道线特征信息并分割出车道线;然后使用多尺度空洞卷积特征融合网络(Multi-scale dilated convolution feature fusion network, MDFN),可以极大扩充模型的感受野,提取全局特征信息;最后使用混合注意力网络(Hybrid attention network, HAN)获取丰富的车道线特征,并增强与当前任务相关的信息。实验结果表明,在TuSimple数据集,该方法检测车道线的准确率为97.6%;在CULane数据集上,该方法在标准路面的检测准确率达到92.5%,多种路面综合检测准确率为75.2%。本文提出的LaneSegNet车道线检测方法分割精确度和推理速度均优于其他模型,且具有更强的适应性和鲁棒性。

    Abstract:

    Lane detection plays an important role in the field of intelligent transportation. Its detection accuracy and speed have an important impact on assisted driving and automatic driving. Aiming at the problems of poor accuracy and slow speed of lane line recognition by deep learning methods, an efficient lane line segmentation method LaneSegNet is proposed. Firstly, based on the principle of encoding and decoding network, a backbone network Lane net is constructed to extract the lane line feature information and segment the lane line; Then, the multi-scale divided convolution feature fusion network is used to greatly expand the receptive field of the model and extract the global feature information; Finally, hybrid attention network is used to obtain rich lane line features and enhance the information related to the current task. The experimental results show that the accuracy of this method is 97.6% in TuSimple data set; On the CULane dataset, the detection accuracy of this method on standard pavement is 92.5%, and the comprehensive detection accuracy of multiple pavement is 75.2%. The LaneSegNet lane line detection method proposed in this paper has better segmentation accuracy and reasoning speed than other models, and has stronger adaptability and robustness.

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  • 收稿日期:2021-10-26
  • 最后修改日期:2021-11-08
  • 录用日期:2021-11-19
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