ProposalLaneNet: Sparse High-Quality Proposal-Driven Efficient Lane Detection

Lane detection is one of the key technologies for local map construction, and it is also a challenging task in intelligent driving, where various computer vision-based methods have been applied to address this issue. However, these methods often suffer from redundancy issues due to the sparse and narrow structure of the lane lines, and full generalization to lane detection needs more effort. To solve these problems, we propose a stepwise positive guidance strategy that utilizes the visually presented lane structure characteristics, which are inspired by the reference points in the DETR-Family methods. This strategy guides the network detection from the reference points to the reference lanes, improving the accuracy of the detection process. Moreover, we propose a new multi-scale feature fusion strategy that directly performs feature fusion on high-quality proposals. This approach differs from traditional object detection models using the Feature Pyramid Network (FPN). It fully uses the sparsity of lanes and reduces the network’s redundant computation. We proposed ProposalLaneNet, which takes full advantage of the lanes’ structure and sparse distribution characteristics. Significant improvements in speed and accuracy have been achieved by our method, enabling it to reach the state-of-the-art performance on the popular datasets CULane and TuSimple. Our method can be used as a new detection paradigm for lane detection.