# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Results
#### 4.2.2 Results on Tusimple  
Table 5 shows a comparison of our model with other lane detection methods on the Tusimple lane detection benchmark dataset. Without the feature post-processing module, the proposed model achieved a top accuracy of 96.47% in terms of F1 score, demonstrating the model's competitive advantage. The Tusimple dataset mainly consists of highway scenes, where methods are near performance saturation, resulting in slight differences in accuracy metrics such as the accuracy (Acc) score.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **F1** & **GFlops** & **Normal** & **Crowd** & **Dazzle** & **Shadow** & **No Line** & **Arrow** & **Curve** & **Cross** & **Night** \\ \hline SCNN [13] & VGG16 & 71.60 & 328.4 & 90.60 & 69.70 & 58.50 & 66.90 & 43.40 & 84.10 & 64.40 & 1990 & 66.10 \\ RESA [14] & ResNet34 & 74.50 & 41.0 & 91.90 & 72.40 & 66.50 & 72.00 & 46.30 & 88.10 & 68.60 & 1896 & 69.80 \\ ENet-SAD [11] & ENet & 70.80 & **3.9** & 90.10 & 68.80 & 60.20 & 65.90 & 41.60 & 84.00 & 65.60 & 1998 & 66.00 \\ PINet [29] & Hourglass & 74.40 & - & 90.30 & 72.30 & 66.30 & 68.40 & 49.80 & 83.70 & 65.60 & 1427 & 67.70 \\ FOLOLane [30] & ERFNet & **78.80** & - & 92.70 & **77.80** & **75.20** & **79.30** & **52.10** & 89.00 & 69.40 & 1569 & **74.50** \\ BezeriLaneNet [16] & ResNet34 & 75.57 & - & 91.59 & 73.20 & 69.20 & 76.74 & 48.05 & 87.16 & 62.45 & **888** & 69.90 \\ ERF-E2 [38] & ERFNet & 74.00 & - & 91.00 & 73.10 & 64.50 & 74.10 & 46.60 & 85.80 & 71.90 & 2022 & 67.90 \\ LaneATT [8] & ResNet34 & 76.68 & 18.0 & 92.14 & 75.03 & 66.47 & 78.15 & 49.39 & 88.38 & 67.72 & 1330 & 70.72 \\ CurveLanes-L [9] & Searched & 74.80 & 86.5 & 90.70 & 72.30 & 67.70 & 70.10 & 49.40 & 85.80 & 68.40 & 1746 & 68.90 \\ UELD [39] & ResNet34 & 72.30 & 16.9 & 90.70 & 70.20 & 59.50 & 69.30 & 44.40 & 85.70 & 69.50 & 2037 & 66.70 \\ UFLD2 [7] & ResNet34 & 75.90 & 20.6 & 92.50 & 74.90 & 65.70 & 75.30 & 49.00 & 88.50 & 70.20 & 1864 & 70.60 \\ CLLD [40] & U-Net & 70.43 & - & 89.80 & 68.39 & 58.93 & 68.86 & 40.68 & 84.50 & 66.20 & 2656 & 71.21 \\ LHFFNet [41] & Hourglass & 75.90 & - & 92.00 & 73.40 & 67.30 & 71.90 & 51.30 & 87.80 & 69.60 & 1401 & 70.60 \\ CBGA [42] & ResNet-34 & 71.00 & - & 90.80 & 70.80 & 61.60 & 71.40 & 44.50 & 86.30 & 65.10 & 2028 & 66.10 \\ GAAC (ours) & ResNet18 & 76.22 & 23.4 & 92.97 & 74.35 & 68.46 & 74.51 & 48.85 & 88.20 & 72.30 & 1785 & 71.63 \\ GAAC (ours) & ResNet34 & 76.41 & 28.6 & **93.05** & 75.26 & 65.65 & 69.75 & 49.41 & **89.01** & **73.27** & 2035 & 72.29 \\ \hline \hline \end{tabular}  
* The comparative data are cited from other studies. ‘-’ means the results are not available. For cross-testing scenarios, only false positives are shown. Boldfaced data indicate that the data rank first in the corresponding metric.  
\end{table}
Table 4: Comparison of mainstream methods and our model on the CULane dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **F1** & **Acc** & **FP** & **FN** \\ \hline LaneNet [12] & H-NET & 94.80 & 96.38 & 7.80 & 2.44 \\ SCNN [13] & VGG16 & 95.97 & 96.53 & 6.17 & **1.80** \\ PointLaneNet [43] & GoogLeNet & 95.07 & 96.34 & 4.67 & 5.18 \\ ENet-SAD [11] & ENet & 95.92 & 96.64 & 6.02 & 2.05 \\ FastDraw [44] & ResNet50 & 93.92 & 95.20 & 7.60 & 4.50 \\ PolyLaneNet [18] & EfficientNet & 90.62 & 93.36 & 9.42 & 9.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of mainstream methods and our model on the Tusimple dataset.