Kao Youchen, Che Shengbing, Zhou Sha, Guo Shenyi, Zhang Xu, Wang Wanqin
School of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.
School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.
Sci Rep. 2024 Jul 16;14(1):16353. doi: 10.1038/s41598-024-66913-1.
Lane line images have the essential attribute of large-scale variation and complex scene information, and the similarity between adjacent lane lines is high, which can easily cause classification errors. And remote lane lines are difficult to recognize due to visual angle changes in width. To address this issue, this paper proposes an effective lane detection framework, which is a hybrid feature fusion network that enhances multiple spatial features and distinguishes key features throughout the entire lane line segment. It enhances and fuses lane line features at multiscale to enhance the feature representation of lane line images, especially at the far end. Firstly, in order to enhance the correlation of multiscale lane features, a multi-head self attention is used to construct a multi-space attention enhancement module for feature enhancement in multispace. Secondly, a spatial separable convolutional branch is designed for the jumping layer structure connecting multiscale lane line features. While retaining feature information of different scales, important lane areas in multiscale feature information are emphasized through the allocation of spatial attention weights. Finally, considering that lane lines are elongated areas in the image, and the background information in the image is much more abundant than lane line information, the flexibility of traditional pooling operations in capturing widely existing anisotropic contexts in actual environments is limited. Therefore, before embedding feature output branches, strip pooling is introduced to refine the representation of lane line information and optimize model performance. The experimental results show that the accuracy on the TuSimple dataset reaches 96.84%, and the F1 score on the CULane dataset reaches 75.9%.
车道线图像具有尺度变化大、场景信息复杂的本质属性，且相邻车道线之间的相似度较高，这很容易导致分类错误。此外，由于视角在宽度上的变化，远距离车道线难以识别。为了解决这个问题，本文提出了一种有效的车道检测框架，它是一种混合特征融合网络，能够增强多个空间特征并在整个车道线段中区分关键特征。它在多尺度上增强并融合车道线特征，以增强车道线图像的特征表示，尤其是在远端。首先，为了增强多尺度车道特征的相关性，使用多头自注意力构建一个多空间注意力增强模块，用于在多个空间中进行特征增强。其次，为连接多尺度车道线特征的跳跃层结构设计了一个空间可分离卷积分支。在保留不同尺度的特征信息的同时，通过空间注意力权重的分配来强调多尺度特征信息中的重要车道区域。最后，考虑到车道线是图像中的细长区域，且图像中的背景信息比车道线信息丰富得多，传统池化操作在捕获实际环境中广泛存在的各向异性上下文方面的灵活性有限。因此，在嵌入特征输出分支之前，引入条带池化来细化车道线信息的表示并优化模型性能。实验结果表明，在TuSimple数据集上的准确率达到96.84%，在CULane数据集上的F1分数达到75.9%。
