Lane detection is one of the fundamental modules in self-driving. In this paper we employ a transformer-only method for lane detection, thus it could benefit from the blooming development of fully vision transformer and achieves the state-of-the-art (SOTA) performance on both CULane and TuSimple benchmarks, by fine-tuning the weight fully pre-trained on large datasets. More importantly, this paper proposes a novel and general framework called PriorLane, which is used to enhance the segmentation performance of the fully vision transformer by introducing the low-cost local prior knowledge. PriorLane utilizes an encoder-only transformer to fuse the feature extracted by a pre-trained segmentation model with prior knowledge embeddings. Note that a Knowledge Embedding Alignment (KEA) module is adapted to enhance the fusion performance by aligning the knowledge embedding. Extensive experiments on our Zjlab dataset show that Prior-Lane outperforms SOTA lane detection methods by a 2.82% mIoU, and the code will be released at: https://github. com/vincentqqb/PriorLane.
翻译:干道探测是自驾驶的基本模块之一。 在本文中, 我们使用一种只使用变压器的变压器来探测车道, 这样它就可以从全视变压器的蓬勃发展中受益, 并且通过微调对大型数据集经过充分预先训练的重量进行微调, 从而在 CULane 和 TuSemple 基准上实现最先进的(SOTA) 性能。 更重要的是, 本文提出了一个叫作PerealLane 的新而通用的框架, 用于通过引入低成本的本地先前知识来提高全视变压器的分层性能。 PrecialLane 使用一个只使用变压器来将经过预先训练的分解模型所提取的功能与先前的知识嵌入的功能连接起来。 注意一个知识嵌入式调整模块( KEA) 来通过整合知识嵌入来增强聚变性能。 对我们的 Zjlab 数据集进行的广泛实验显示, 前Lane 用2.82% MIOU 超越SETA 航道探测方法, 代码将在以下发布: http://githhuhubub. com/ pliccentqqqqrane. qrane.