Grid Anchor Lane Detection Based on Attribute Correlation
The detection of road features is a necessary approach to achieve autonomous driving. And lane lines are important two-dimensional features on roads, which are crucial for achieving autonomous driving. Currently, research on lane detection mainly focuses on the positioning detection of local feature...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/699 |
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| author | Qiaohui Feng Cheng Chi Fei Chen Jianhao Shen Gang Xu Huajie Wen |
| author_facet | Qiaohui Feng Cheng Chi Fei Chen Jianhao Shen Gang Xu Huajie Wen |
| author_sort | Qiaohui Feng |
| collection | DOAJ |
| description | The detection of road features is a necessary approach to achieve autonomous driving. And lane lines are important two-dimensional features on roads, which are crucial for achieving autonomous driving. Currently, research on lane detection mainly focuses on the positioning detection of local features without considering the association of long-distance lane line features. A grid anchor lane detection model based on attribute correlation is proposed to address this issue. Firstly, a grid anchor lane line expression method containing attribute information is proposed, and the association relationship between adjacent features is established at the data layer. Secondly, a convolutional reordering upsampling method has been proposed, and the model integrates the global feature information generated by multi-layer perceptron (MLP), achieving the fusion of long-distance lane line features. The upsampling and MLP enhance the dual perception ability of the feature pyramid network in detail features and global features. Finally, the attribute correlation loss function was designed to construct feature associations between different grid anchors, enhancing the interdependence of anchor recognition results. The experimental results show that the proposed model achieved first-place F1 scores of 93.05 and 73.27 in the normal and curved scenes on the CULane dataset, respectively. This model can balance the robustness of lane detection in both normal and curved scenarios. |
| format | Article |
| id | doaj-art-ea0fc516cde545c28ef2bf137792e1c3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ea0fc516cde545c28ef2bf137792e1c32025-01-24T13:20:31ZengMDPI AGApplied Sciences2076-34172025-01-0115269910.3390/app15020699Grid Anchor Lane Detection Based on Attribute CorrelationQiaohui Feng0Cheng Chi1Fei Chen2Jianhao Shen3Gang Xu4Huajie Wen5Shenzhen Technology University, Shenzhen 518118, ChinaShenzhen Technology University, Shenzhen 518118, ChinaShenzhen Technology University, Shenzhen 518118, ChinaShenzhen Technology University, Shenzhen 518118, ChinaCollege of Applied Technology, Shenzhen University, Shenzhen 518118, ChinaCollege of Applied Technology, Shenzhen University, Shenzhen 518118, ChinaThe detection of road features is a necessary approach to achieve autonomous driving. And lane lines are important two-dimensional features on roads, which are crucial for achieving autonomous driving. Currently, research on lane detection mainly focuses on the positioning detection of local features without considering the association of long-distance lane line features. A grid anchor lane detection model based on attribute correlation is proposed to address this issue. Firstly, a grid anchor lane line expression method containing attribute information is proposed, and the association relationship between adjacent features is established at the data layer. Secondly, a convolutional reordering upsampling method has been proposed, and the model integrates the global feature information generated by multi-layer perceptron (MLP), achieving the fusion of long-distance lane line features. The upsampling and MLP enhance the dual perception ability of the feature pyramid network in detail features and global features. Finally, the attribute correlation loss function was designed to construct feature associations between different grid anchors, enhancing the interdependence of anchor recognition results. The experimental results show that the proposed model achieved first-place F1 scores of 93.05 and 73.27 in the normal and curved scenes on the CULane dataset, respectively. This model can balance the robustness of lane detection in both normal and curved scenarios.https://www.mdpi.com/2076-3417/15/2/699lane detectionanchorFPNdeep learning |
| spellingShingle | Qiaohui Feng Cheng Chi Fei Chen Jianhao Shen Gang Xu Huajie Wen Grid Anchor Lane Detection Based on Attribute Correlation Applied Sciences lane detection anchor FPN deep learning |
| title | Grid Anchor Lane Detection Based on Attribute Correlation |
| title_full | Grid Anchor Lane Detection Based on Attribute Correlation |
| title_fullStr | Grid Anchor Lane Detection Based on Attribute Correlation |
| title_full_unstemmed | Grid Anchor Lane Detection Based on Attribute Correlation |
| title_short | Grid Anchor Lane Detection Based on Attribute Correlation |
| title_sort | grid anchor lane detection based on attribute correlation |
| topic | lane detection anchor FPN deep learning |
| url | https://www.mdpi.com/2076-3417/15/2/699 |
| work_keys_str_mv | AT qiaohuifeng gridanchorlanedetectionbasedonattributecorrelation AT chengchi gridanchorlanedetectionbasedonattributecorrelation AT feichen gridanchorlanedetectionbasedonattributecorrelation AT jianhaoshen gridanchorlanedetectionbasedonattributecorrelation AT gangxu gridanchorlanedetectionbasedonattributecorrelation AT huajiewen gridanchorlanedetectionbasedonattributecorrelation |