Sparse LaneFormer: End-to-End Lane Detection With Sparse Proposals and Interactions

Lane detection is a challenging task due to the inherently sparse nature of lanes, which are characterized by their thin and long structures. However, existing lane detection methods often rely on dense anchors or interactions and may not well handle the sparse characteristics of lanes. Dense anchor-based methods are not fully end-to-end, necessitating non-maximum suppression to eliminate near-duplicate detection results. Recently, DETR-like methods have emerged, aiming to achieve end-to-end lane detection with sparse queries. Nevertheless, these methods establish dense interactions among all image tokens, which not only leads to substantial computational redundancy, but also complicates the extraction of sparse lane features from global feature maps. To tackle these issues, we propose a novel end-to-end lane detection framework called Sparse LaneFormer, which leverages both sparse proposals and interactions. Specifically, we introduce a Lane Aware Query (LAQ) as sparse lane proposals, which can predict learnable and explicit lane positions. Additionally, we introduce a Lane Token Selector (LTS) and a Linear Deformable Sampler (LDS) to establish sparse interactions among lane tokens. LTS selectively updates sparse lane tokens in the encoder, while LDS, in the decoder, extracts discriminative lane representations from global feature maps by sampling lane features along LAQ. By concentrating on the most informative tokens, Sparse LaneFormer can achieves state-of-the-art performance with only 10% of encoder tokens, resulting in an 80% reduction in overall computational cost compared to its dense counterpart. Our Sparse LaneFormer represents a pioneering effort in exploring a fully sparse design for lane detection, setting a new benchmark for efficiency and accuracy in this field.