Opto-Electronic Engineering, Volume. 50, Issue 7, 230079(2023)
Axial attention-guided anchor classification lane detection
Figures & Tables(13)
![Description of lane line definition and selection of row anchor and column anchor. (a) The definition of lane line in CULane dataset[2]; (b) Left ego lane and right ego lane; (c) Left side lane and right side lane](/Images/icon/loading.gif){kind=icon}
Fig. 2. Description of lane line definition and selection of row anchor and column anchor. (a) The definition of lane line in CULane dataset[2]; (b) Left ego lane and right ego lane; (c) Left side lane and right side lane
Fig. 6. Schematic diagram of the attention structure of the axial. It includes two multi-head attention mechanisms
Table 1. Datasets description
View tableView in ArticleTable 1. Datasets description
数据集 总数据 训练集 验证集 测试集 分辨率 车道数 环境 场景 TuSimple 6408 3268 358 2782 1280×720 ≤5 1 高速公路 CULane 133235 88880 9675 34680 1640×590 ≤4 9 城区和高速公路
Table 2. Hyperparameter settings on different datasets.
View tableView in ArticleTable 2. Hyperparameter settings on different datasets.
数据集 TuSimple CULane 行数量 56 18 列数量 40 40 每一行单元格数量(行锚点数量) 100 200 每一列单元格数量(列锚点数量) 100 100 使用行锚分类车道线数量 2 2 使用列锚分类车道线数量 2 2
Table 3. Ablation results
View tableView in ArticleTable 3. Ablation results
行锚 列锚 轴注意力 精度/TuSimple 精度/CULane √ 95.55 64.72 √ √ 95.89 71.34 √ √ 95.91 65.61 √ √ √ 95.92 73.05
Table 4. Comparison with other methods on the TuSimple dataset
View tableView in ArticleTable 4. Comparison with other methods on the TuSimple dataset
方法 F1/% Acc/% FP/% FN/% 注：N/A表示相关论文没有提及该内容。 基于分割的方法 SCNN[ 2 ]95.97 96.53 6.17 1.80 SAD[ 6 ]95.92 96.64 6.02 2.05 LaneNet[ 7 ]N/A 96.40 7.80 2.44 DALaneNet[ 8 ]N/A 95.86 8.20 3.16 基于参数曲线的方法 BezierLaneNet (ResNet34)[ 15 ]N/A 95.65 5.10 3.90 基于检测的方法 E2E (ResNet34)[ 10 ]N/A 96.22 3.21 4.28 LaneATT (ResNet18)[ 14 ]96.71 95.57 3.56 3.01 UFLD (ResNet34)[ 11 ]N/A 95.55 19.35 4.30 UFLDv2 (ResNet18)[ 12 ]96.11 95.92 3.16 4.59 Ours (ResNet34) 96.64 95.92 2.41 4.29
Table 5. Comparison of F1 on CULane dataset
View tableView in ArticleTable 5. Comparison of F1 on CULane dataset
方法 Normal Crowd Dazzle Shadow No-line Arrow Curve Cross Night Average FPS 注：N/A表示相关论文没有提及该内容。 基于分割的方法 SCNN[ 2 ]90.6 69.7 58.5 66.9 43.4 84.1 64.4 1990 66.1 71.6 8 SAD[ 6 ]90.1 68.8 60.2 65.9 41.6 84.0 65.7 1998 66.0 70.8 75 基于参数曲线的方法 BezierLaneNet (ResNet18)[ 15 ]90.2 71.6 62.5 70.9 45.3 84.1 59.0 996 68.7 73.7 N/A 基于检测的方法 E2E (ResNet34)[ 10 ]90.4 69.9 61.5 68.1 45.0 83.7 69.8 2077 63.2 71.5 N/A CLRNet (ResNet34)[ 13 ]93.3 78.3 73.7 79.7 53.1 90.3 71.6 1321 75.1 76.9 103 LaneATT (ResNet18)[ 14 ]91.1 73.0 65.7 70.9 48.4 85.5 68.4 1170 69.0 75.1 250 LaneATT (ResNet34)[ 14 ]92.1 75.0 66.5 78.2 49.4 88.4 67.7 1330 70.7 76.7 171 UFLD (ResNet18)[ 11 ]89.3 68.0 62.2 63.0 40.7 83.5 58.2 1743 62.9 69.7 323 UFLD (ResNet34)[ 11 ]89.5 68.7 57.2 69.2 41.7 84.7 59.3 2037 65.4 70.9 175 UFLDv2 (ResNet18)[ 12 ]92.0 74.0 63.2 72.4 45.0 87.7 69.0 1998 69.8 75.0 330 Ours (ResNet34) 92.6 74.9 65.6 75.5 49.0 88.2 69.8 1864 70.9 76.0 171
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Xin Luo, Yingping Huang, Zhenming Liang. Axial attention-guided anchor classification lane detection[J]. Opto-Electronic Engineering, 2023, 50(7): 230079
Paper Information
Category: Article
Received: Apr. 7, 2023
Accepted: Jul. 11, 2023
Published Online: Sep. 25, 2023
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