In this paper, we present a dense hybrid proposal modulation (DHPM) method for lane detection. Most existing methods perform sparse supervision on a subset of high-scoring proposals, while other proposals fail to obtain effective shape and location guidance, resulting in poor overall quality. To address this, we densely modulate all proposals to generate topologically and spatially high-quality lane predictions with discriminative representations. Specifically, we first ensure that lane proposals are physically meaningful by applying single-lane shape and location constraints. Benefitting from the proposed proposal-to-label matching algorithm, we assign each proposal a target ground truth lane to efficiently learn from spatial layout priors. To enhance the generalization and model the inter-proposal relations, we diversify the shape difference of proposals matching the same ground-truth lane. In addition to the shape and location constraints, we design a quality-aware classification loss to adaptively supervise each positive proposal so that the discriminative power can be further boosted. Our DHPM achieves very competitive performances on four popular benchmark datasets. Moreover, we consistently outperform the baseline model on most metrics without introducing new parameters and reducing inference speed.

翻译：本文提出了一种用于车道检测的密集混合提议调制（DHPM）方法。现有方法大多仅对高分提议子集进行稀疏监督，而其他提议无法获得有效的形状与位置引导，导致整体质量欠佳。为解决这一问题，我们对所有提议进行密集调制，以生成具有判别性表征的拓扑与空间高质量车道预测。具体而言，我们首先通过施加单车道形状与位置约束，确保车道提议具有物理意义。得益于所提出的提议-标签匹配算法，我们为每个提议分配一个目标真实车道，从而高效学习空间布局先验。为增强泛化能力并建模提议间关系，我们对匹配同一真实车道的提议进行形状差异多样化处理。除形状与位置约束外，我们还设计了一种质量感知分类损失，以自适应地监督每个正例提议，从而进一步提升判别能力。我们的DHPM在四个主流基准数据集上取得了极具竞争力的性能。此外，在不引入新参数且不降低推理速度的前提下，我们在多数指标上持续优于基线模型。