Exploiting the Complementarity of Bilateral Domains for Fast Lane Detection

Lane detection plays a crucial role in the visual perception system of intelligent driving, aiming to rapidly identify various lane lines embedded in complex road scenarios. However, accurately and quickly detecting lane lines remains a challenging task, especially with the limited representation capacity of spatial domain. Using frequency to guide the few-visual-clue lane detection in spatial domain can be a cure, as frequency domain effectively describes sparse lane local contexts from a complementary perspective. To achieve accurate and fast lane detection, we propose a novel network that smoothly introduces frequency space into the spatial domain. We first design two light-weight modules, i.e., the Domain Transformation Module (DTM) and the Bilateral Aggregation Module (BAM), to explicitly perceive lane features with diverse semantics in bilateral domains. Concretely, the DTM excites lane local patterns in frequency space via a parallel sub-convolutions manner, while the BAM selectively absorbs informative components from the intra- and inter-domain perspectives. We then devise a small parametric module, named Position Refinement Module (PRM), to model fine-grained lane locations. It is instantiated into the last three stages of network to reconstruct detailed positional relationships by encoding global semantics and local contexts into unified lane embeddings. Extensive experiments on two widely-used datasets show that our method significantly outperforms the state-of-the-art approaches. Especially, our method achieves a superior inference efficiency of 0.011 second per image along with a total F1 score of 79.28% on the CULane dataset.