ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicAI-Driven Architectures and Algorithms for Secure and Scalable Big Data SystemsView all 5 articles
StaBle-MambaNet: Structure-Aware and Blur-Guided Lane Detection with Mamba
Provisionally accepted
- 1Shenyang University of Technology, Shenyang, China
- 2China United Network Communications Co Ltd, Beijing, China
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The perception system constitutes a critical component of autonomous driving , due to factors such as high-speed motion and complex illumination, camera-captured images often exhibit local blurring, leading to the degradation of lane structure clarity and even temporary disappearance of lane markings, which severely compromises the accuracy and robustness of lane detection. Traditional approaches typically adopt a two-stage strategy of "image enhancement followed by structural recognition" Initially, the entire image undergoes deblurring or super-resolution reconstruction, followed by lane detection. However, such methods rely on the quality of full-image restoration, exhibit low processing efficiency, and struggle to determine whether the disappearance of lane markings is genuinely caused by image blurring. To address these challenges, this paper proposes an Inter-frame Stability-Aware Blur-enhanced Mamba Network (StaBle-MambaNet), which identifies blurred regions and assesses the presence of potential lane structures without relying on full-image restoration. The method first localizes blurred areas and employs a Structure-Aware Restoration Module to perform directional extrapolation and completion for potential lane line regions. Subsequently, the Blur-Guided Consistency Reasoning Module evaluates structural stability to identify genuine lane regions. Finally, enhanced features are constructed into a spatially continuous token sequence, which is fed into a lightweight state-space model, Mamba, to model the dynamic feature variations in blurred regions while preserving the vertical structural evolution of the image. Experimental results demonstrate that StaBle-MambaNet significantly outperforms existing mainstream methods across multiple public lane datasets (e.g., CULane and CurveLanes), particularly under challenging conditions such as nighttime, occlusion, and curved lanes, exhibiting clear advantages in both detection accuracy and structural stability.
Keywords: Lane detection, Blurred Scenes, Structural confirmation, feature completion, temporal modeling, Blur-AwareRepresentation
Received: 18 Aug 2025; Accepted: 29 Oct 2025.
Copyright: © 2025 Zhang, Hongwei, Peng, Zhang, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiting Peng, xt.peng@sut.edu.cn
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