基于混合注意机制和行锚分类的无人驾驶地理信息感知车道线检测算法

Yongchao Song, Tao Huang, Xin Fu, Yahong Jiang, Jindong Xu, Jindong Zhao, Weiqing Yan, X. Wang
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引用次数: 1

摘要

车道线检测是实现无人驾驶和高级辅助驾驶地理信息感知的基础和关键任务。然而,传统的车道线检测方法依赖于人工调整参数,通用性差,工作量大,鲁棒性差。大多数基于深度学习的方法很难有效地平衡准确性和效率。为了提高自然交通环境下车道线地理信息的综合感知能力,提出了一种基于混合关注机制残差网络(ResNet)和行锚分类的车道线检测算法。在主干网卷积层、归一化层和激活层之后分别加入混合关注机制,使模型更加关注重要的车道线特征,提高特征提取的针对性和效率。此外,为了实现更快的检测速度和解决无视觉问题,采用基于行方向的车道线位置选择分类方法,根据行锚点检测每个候选点是否存在车道线,降低了传统语义分割在逐像素基础上分割带来的高计算复杂度。基于TuSimple和CurveLane数据集,对多场景、多环境、多线性道路图像数据集和视频序列进行了集成和自建,并设计和测试了多个实验,验证了该方法的有效性。混合注意机制网络模型的测试精度达到95.96%,平均时间效率接近180 FPS,可以实现高水平的准确率和实时性检测过程。因此,所提出的方法能够满足自然交通环境下车道线地理信息的安全感知效果,实现实际道路应用场景的准确性与效率之间的有效平衡。
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A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification
Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios.
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