Lane detection algorithm based on adaptive segmentation network

doi:10.3969/j.issn.1674-8484.2025.03.014

Abstract

Abstract:

Aiming to address the issues of reliance on prior knowledge and limited adaptability in traditional image segmentation approaches within tunnel scenarios, a lane line detection method was proposed based on an adaptive segmentation network. Firstly, a sub-region planning method based on illumination characteristics was designed, which adaptively determined the necessity of multi-region segmentation by extracting illumination feature signals and provided the corresponding sub-region configuration scheme in real time. Secondly, a lane line area segmentation method was proposed based on improved Otsu. Each sub-region can independently adjust the segmentation threshold according to the lighting characteristics to achieve precise segmentation of the lane line area. Finally, a dynamic region of interest update method was designed to update the region of interest (ROI) of the current frame based on the detection results of the previous frame. The results show that the detection accuracy of the proposed algorithm reaches 96.73%, and the average processing time per frame is 24.77 ms in typical tunnel scenarios such as complex lighting, low illumination, and discontinuous lane lines, indicating that the proposed method has the advantages in detection accuracy, detection efficiency and robustness, and can meet the needs of real-time performance.

Key words: automobile test, lane detection, image segmentation, adaptive segmentation, Otsu algorithm, dynamic region of Interest (ROI)

CLC Number:

U467.4

WANG Yifei, LI Yonghang, ZHANG Yali, WANG Chang, WANG Taiqi, YUAN Huazhi. Lane detection algorithm based on adaptive segmentation network[J]. Journal of Automotive Safety and Energy, 2025, 16(3): 478-486.

Figures/Tables 15

References 23

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检测方法	正确检测车道数	准确率/ %	平均处理时间/ ms
文献[8]	3 681	87.10	45.29
文献[8]	5 628	91.30	45.29
文献[23]	3 681	87.10	43.23
文献[23]	5 826	94.52	43.23
传统图像分割	3 830	90.63	29.97
传统图像分割	5 717	92.75	29.97
本文算法	4 088	96.73	24.77
本文算法	5 951	96.54	24.77

检测方法	正确检测车道数	准确率/ %	平均处理时间/ ms
文献[8]	3 681	87.10	45.29
文献[8]	5 628	91.30	45.29
文献[23]	3 681	87.10	43.23
文献[23]	5 826	94.52	43.23
传统图像分割	3 830	90.63	29.97
传统图像分割	5 717	92.75	29.97
本文算法	4 088	96.73	24.77
本文算法	5 951	96.54	24.77