Abstract
Lane detection with front-view RGB images has been a long-standing challenge. Among the various methods, curve-based approaches are known for their fast speed, conciseness, and ability to handle occlusions. However, these methods often suffer from a relative low accuracy, attributing to the inflexibility of adopted curve model, the inefficient lane feature extraction, and a rigid curve regression supervision. In this paper, we propose a novel curve-based lane detection method that addresses these limitations. The lane lines are modeled with B-splines, which provide greater flexibility. Explicit spatial attention maps are used to guide the network in extracting relevant lane features from the image. Additionally, a layer-by-layer refinement process is employed to improve the lane predictions. Importantly, the ground truth of spatial attention maps also serve as pixel-level supervision for the lane instances. We evaluate the proposed method on four widely used lane detection datasets and demonstrate the state-of-the-art performance achieved among curve-based approaches on CULane and LLAMAS dataset.









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Acknowledgements
Project is supported by the Natural Science Foundation of Chongqing [CSTB2023NSCQ-MSX0063], the original research project of Tongji University [22120220593], the National Key R&D Program of China [2021YFB2501104], the Shanghai Municipal Science and Shanghai Automotive Industry Science and Technology Development Foundation [2407].
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Concept: W. Tian; Method: W. Tian, Y. Han, Y. Huang, X. Yu; Implementation: Y. Han; Writing: W. Tian, Y. Han; Review: W. Tian, Y. Huang; Project management: W. Tian.
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Tian, W., Han, Y., Huang, Y. et al. Refinecurvelane: lane detection with B-spline curve in a layer-by-layer refinement manner. Multimedia Systems 30, 343 (2024). https://doi.org/10.1007/s00530-024-01557-9
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DOI: https://doi.org/10.1007/s00530-024-01557-9