Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location
Lane detection is the critical sensing technology for autonomous driving systems and advanced driving assistance systems. Along with the rapid evolution of deep learning, vision-based lane detection has made tremendous progress. However, it still faces significant challenges in complex scenarios lac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10145461/ |
| _version_ | 1797803140585619456 |
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| author | Shoubiao Li Xin Wu Zhifei Wu |
| author_facet | Shoubiao Li Xin Wu Zhifei Wu |
| author_sort | Shoubiao Li |
| collection | DOAJ |
| description | Lane detection is the critical sensing technology for autonomous driving systems and advanced driving assistance systems. Along with the rapid evolution of deep learning, vision-based lane detection has made tremendous progress. However, it still faces significant challenges in complex scenarios lacking visual clues, such as severe occlusions and extreme lighting conditions. To detect multiple lanes accurately and efficiently in challenging scenarios, we propose a novel multi-lane detection method called Large-kernel Lane Network (LkLaneNet). By fusing factorized convolution and depth-wise convolution, the Efficient Large-kernel Convolution Module (ELCM) is designed to increase the effective receptive field of the network. Thus, more helpful information can be gathered from a larger region for accurate lane detection. In addition, a location-based instance detection approach is proposed to flexibly distinguish different lanes using the lane start locations at the image boundaries, coupled with the row-wise classification formulation for efficient multi-lane detection. The proposed method is evaluated on two popular lane detection benchmarks, CULane and TuSimple. The results show that our method can achieve advanced performance in complex scenarios while maintaining real-time detection efficiency. |
| first_indexed | 2024-03-13T05:16:21Z |
| format | Article |
| id | doaj.art-9c652ef11c754bb0a66d8609bd275645 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T05:16:21Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9c652ef11c754bb0a66d8609bd2756452023-06-15T23:01:05ZengIEEEIEEE Access2169-35362023-01-0111581255813510.1109/ACCESS.2023.328344010145461Efficient Multi-Lane Detection Based on Large-Kernel Convolution and LocationShoubiao Li0https://orcid.org/0000-0002-5860-8786Xin Wu1https://orcid.org/0000-0001-8678-9505Zhifei Wu2https://orcid.org/0000-0002-8511-0123College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaLane detection is the critical sensing technology for autonomous driving systems and advanced driving assistance systems. Along with the rapid evolution of deep learning, vision-based lane detection has made tremendous progress. However, it still faces significant challenges in complex scenarios lacking visual clues, such as severe occlusions and extreme lighting conditions. To detect multiple lanes accurately and efficiently in challenging scenarios, we propose a novel multi-lane detection method called Large-kernel Lane Network (LkLaneNet). By fusing factorized convolution and depth-wise convolution, the Efficient Large-kernel Convolution Module (ELCM) is designed to increase the effective receptive field of the network. Thus, more helpful information can be gathered from a larger region for accurate lane detection. In addition, a location-based instance detection approach is proposed to flexibly distinguish different lanes using the lane start locations at the image boundaries, coupled with the row-wise classification formulation for efficient multi-lane detection. The proposed method is evaluated on two popular lane detection benchmarks, CULane and TuSimple. The results show that our method can achieve advanced performance in complex scenarios while maintaining real-time detection efficiency.https://ieeexplore.ieee.org/document/10145461/Lane detectionlarge-kernel convolutioninstance detectionrow-wise classificationdeep learning |
| spellingShingle | Shoubiao Li Xin Wu Zhifei Wu Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location IEEE Access Lane detection large-kernel convolution instance detection row-wise classification deep learning |
| title | Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location |
| title_full | Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location |
| title_fullStr | Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location |
| title_full_unstemmed | Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location |
| title_short | Efficient Multi-Lane Detection Based on Large-Kernel Convolution and Location |
| title_sort | efficient multi lane detection based on large kernel convolution and location |
| topic | Lane detection large-kernel convolution instance detection row-wise classification deep learning |
| url | https://ieeexplore.ieee.org/document/10145461/ |
| work_keys_str_mv | AT shoubiaoli efficientmultilanedetectionbasedonlargekernelconvolutionandlocation AT xinwu efficientmultilanedetectionbasedonlargekernelconvolutionandlocation AT zhifeiwu efficientmultilanedetectionbasedonlargekernelconvolutionandlocation |