Gao Rong, Hu Siqi, Yan Lingyu, Zhang Lefei, Wu Jia
School of Computer Science, Hubei University of Technology, Wuhan, 430068, China; State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing, 210023, China.
School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
Neural Netw. 2025 Jul;187:107347. doi: 10.1016/j.neunet.2025.107347. Epub 2025 Mar 15.
Benefiting from the booming development of Transformer methods, the performance of lane detection tasks has been rapidly improved. However, due to the influence of inaccurate lane line shape constraints, the query sequences of existing transformer-based lane line detection methods contain a large number of repetitive and invalid information regions, which leads to redundant information in the detection region and makes the processing of information on localized feature details of the lanes biased. In this paper, a multi-granularity perceptual query attention transformer lane detection method, CFI-Former, is proposed to achieve more accurate lane detection. Specifically, a multi-granularity perceptual query attention (GQA) module is designed to extract lane local detail information. By a two-stage query from coarse to fine, redundant key-value pairs with low information relevance are first filtered out, and then fine-grained token-to-token attention is executed on the remaining candidate regions. This module emphasizes the multi-granularity nuances of lane features from global to local, leading to more effective models based on lane line shape constraints. In addition, weighted adaptive LIoU loss (L) is proposed to improve lane detection in more challenging scenarios by adaptively increasing the relative gradient of high IoU lane objects and the weight of the loss. Extensive experiments show that CFI-Former outperforms the baseline on two popular lane detection benchmark datasets.
受益于Transformer方法的蓬勃发展，车道检测任务的性能得到了快速提升。然而，由于车道线形状约束不准确的影响，现有的基于Transformer的车道线检测方法的查询序列包含大量重复和无效的信息区域，这导致检测区域中存在冗余信息，并使得对车道局部特征细节信息的处理产生偏差。本文提出了一种多粒度感知查询注意力Transformer车道检测方法CFI-Former，以实现更精确的车道检测。具体来说，设计了一个多粒度感知查询注意力（GQA）模块来提取车道局部细节信息。通过从粗到细的两阶段查询，首先过滤掉信息相关性低的冗余键值对，然后对剩余的候选区域执行细粒度的token-to-token注意力。该模块强调了车道特征从全局到局部的多粒度细微差别，从而基于车道线形状约束得到更有效的模型。此外，还提出了加权自适应LIoU损失（L），通过自适应增加高IoU车道对象的相对梯度和损失权重，在更具挑战性的场景中改进车道检测。大量实验表明，CFI-Former在两个流行的车道检测基准数据集上优于基线。
