# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 4 Experiments
### Dataset  
TuSimple and CULane are two popular lane detection benchmarks datasets. TuSimple is relatively simple, and mainly consists of the highway environment with few obstacles, whereas CULane covers various scenarios of the urban road. Table 1 gives the basic information for the two datasets.  
(1) TuSimple: TuSimple is one of the large-scale datasets currently used for lane detection tasks. It consists of 3626 training images and 2782 test images. The scenarios are mainly the highway scenarios at different times of the day under good weather conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Resolution** & **Road Type** & **\# Lane** \\ \hline \hline \multirow{2}{*}{Tusimple} & 3626 & 2782 & \(1280\times 720\) & highway & \(\leq\)5 \\  & 88,880 & 34,680 & \(1640\times 590\) & urban, rural and highway & \(\leq\)4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of the datasets.  
Figure 6: Association embedding of key points. \(L_{assemble}\) enables the embeddings of these key points as close as possible to the cluster center of this lane \(\mu_{l}\). \(L_{separate}\) is used to separate the cluster centers of different lanes.  
The accuracy is the main evaluation metric of this dataset, and it is defined as the average number of correctly predicted points in the current image. The formula is as follows:  
\[accuracy=\sum_{im}\frac{C_{im}}{S_{im}}\, \tag{8}\]  
where \(C_{im}\) denotes the number of correctly predicted points in the current image, and \(S_{im}\) denotes the number of ground-truth points in the image. When the difference between the ground-truth point and the predicted point is less than a certain threshold, the point is considered to be correctly predicted. In addition, the false positive (FP) and the false negative (FN) scores are provided:  
\[FP=\frac{F_{pred}}{N_{pred}}\, \tag{9}\]  
\[FN=\frac{M_{pred}}{N_{gt}}\, \tag{10}\]  
where \(F_{pred}\) denotes the number of wrongly predicted lanes, \(N_{pred}\) denotes the number of predicted lanes, \(M_{pred}\) denotes the number of missed lanes and \(N_{gt}\) denotes the number of ground-truth lanes.  
(2) CULane: CULane contains 88,880 training images, 9675 validation images and 34,680 test images, covering various scenarios and road types (such as urban and night). At this dataset, the F1-score is the main evaluation metric, and the formula is defined as follows:  
\[F1=2*\frac{Precision*Recall}{Precision+Recall}\, \tag{11}\]  
where \(Precision=\frac{TP}{TP+FP}\) denotes the proportion of the samples with \(FP\) in all samples with the predicted value of 1, and \(Recall=\frac{TP}{TP+FN}\) denotes the proportion of the samples with \(FP\) in all samples with the ground-truth value of 1. \(TP\) is the true positive, which means the samples with the predicted value of 1 and the ground-truth value of 1. \(FP\) is the false positive, which means the samples with the predicted value of 1 but the true value of 0. \(FN\) is the false negative, which means the samples with the predicted value of 0 but the true value of 1.