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Research on Lane Line Detection Algorithm Based on Lightweight U2-Net
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Huan Deng1, Jian Wang1, Mengjun Wu2, Ruofei Du1, Mingzhe Fei1, Yunjing Wang1
Automotive Engineer | 2024, (8) : 22 - 28
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Automotive Engineer | 2024, (8): 22-28
• Special Issue on Intelligent Vehicle Environmental Perception and Target Detection Technology •
Research on Lane Line Detection Algorithm Based on Lightweight U2-Net
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Huan Deng1, Jian Wang1, Mengjun Wu2, Ruofei Du1, Mingzhe Fei1, Yunjing Wang1
Affiliations
  • 1 Shandong Jiaotong University, Jinan 250357
  • 2 Ningbo Yinzhou DLT Technologies Co., Ltd., Ningbo 315100
Published: 2024-08-15 doi: 10.20104/j.cnki.1674-6546.20230435
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In response to the multilane lines driving environments such as lane line occlusion, road shadows, where the extractes lane line feature information is missing, resulting in blurry and discontinuous predicted lane lines, this paper proposes a lightweight U2-Net network for lane line detection algorithm. Firstly, the Residual U-blocks (RSU) module of the lightweight U2-Net network and multi feature scale fusion are used to obtain globally informative lane line features; secondly, pixel-by-pixel threshold judgment is performed on lane line features, and the least square method is selected combined with the lane line cluster of Region Of Interest (ROI) to fit lane line, to achieve multilane line detection and determine the self lane line area; finally, the proposed lane detection algorithm is validated and analyzed in the TuSimple dataset. The results show that the average accuracy of the proposed lane line detection algorithm reaches 98.4%. Compared with other lane line detection networks, this algorithm has fewer network parameters and higher accuracy.

Lightweight U2-Net  /  Residual U-blocks (RSU)  /  Multi-lane line detection  /  Self lane line
Huan Deng, Jian Wang, Mengjun Wu, Ruofei Du, Mingzhe Fei, Yunjing Wang. Research on Lane Line Detection Algorithm Based on Lightweight U2-Net[J]. Automotive Engineer, 2024 , (8) : 22 -28 . DOI: 10.20104/j.cnki.1674-6546.20230435
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Article Info
doi: 10.20104/j.cnki.1674-6546.20230435
  • Online Date：2025-11-25
  • Published：2024-08-15
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  • Revised：2023-10-21
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    1 Shandong Jiaotong University, Jinan 250357
    2 Ningbo Yinzhou DLT Technologies Co., Ltd., Ningbo 315100
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