| 李梅梅,胡春海,龙 平,刘少楠.基于 MultiRes+UNet 网络的车道线检测算法[J].电子测量与仪器学报,2020,34(9):117-122 |
| 基于 MultiRes+UNet 网络的车道线检测算法 |
| Lane line detection algorithm based on MultiRes+UNet network |
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| DOI： |
| 中文关键词: 车道线检测 MultiRes+UNet 网络 空洞卷积 深度学习 |
| 英文关键词:lane detection MultiRes+UNet network dilated convolution deep learning |
| 基金项目:国家自然科学基金(61601105,61602099,61701097)、河北省自然科学基金(F2016501073)资助项目 |
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| 摘要点击次数: 326 |
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| 中文摘要: |
| 无人驾驶技术改变人类生活方式,带车道线属性的高精地图,是无人驾驶领域的重要一环。 针对现有算法在车道线检
测时存在准确率低、效率低等问题提出基于 MultiRes+UNet 检测方法。 该方法通过空洞卷积扩大卷积感受野,从而对全局信息
统筹,运用 MultiRes block 和 Res path 结构减轻编码器-解码器特征之间的差异,大大降低了内存的需求。 实验结果表明,此算
法在保证检测准确率的同时, 提高了算法的稳定性和运行速率,在纯车道、复合车道、阴影污损车道等多情况下,调和平均值分
数分别为 0. 959、0. 942、0. 891,该算法存在高效性、高鲁棒性。 |
| 英文摘要: |
| Unmanned technology changes human lifestyle. The high-precision map with the lane line attribute plays a crucial role in the
unmanned field. Proposing a detection method based on MultiRes + UNet network, aiming at the problem that the low accuracy and low
efficiency of existing algorithms in the detection of composite lane lines. This method expands the convolution receptive field by dilated
convolution to co-ordinate global information. The MultiRes block and Res path structure are used to reduce the difference between the
encoder and decoder features, which greatly reduces the memory requirement. The experimental results show that the proposed algorithm
improves the detection speed of the algorithm while ensuring the detection accuracy. When the pure lane, compound lane and shadow
fouling lanes are obtained, the harmonic mean F1 scores are 0. 959, 0. 942, and 0. 891, the algorithm is high efficiency and high
robustness. |
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