# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 4 Results
### Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Stop** & **StopLeft** & **Go** & **GoLeft** & **Warning** & **WarningLeft** & **All** \\ \hline Color detector & - & - & - & - & - & - & 0.04 \\ Spot detector & - & - & - & - & - & - & 0.0004 \\ ACF detector & - & - & - & - & - & - & 0.36 \\ Faster R-CNN & 0.14 & 0.01 & 0.19 & 0.001 & - & - & 0.09 \\ SLD & 0.08 & - & 0.10 & - & - & - & 0.09 \\ Modified ACF detector & 0.63 & 0.13 & 0.40 & 0.37 & - & - & 0.38 \\ Multi-detector & 0.72 & 0.28 & 0.52 & 0.40 & - & - & 0.48 \\ Our approach & 0.70 & 0.40 & 0.88 & 0.71 & 0.52 & 0.24 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: LISA daytime dataset test results (mAP). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **YOLOv3 +** & **YOLOv3 +** & **Unified** & **Unified** \\  & **AlexNet** & **YOLOv3-tiny + LeNet** & **Network** & **Network** \\ \hline Data augmentation & - & - & - & \(\checkmark\) \\ mAP & 0.36 & 0.55 & 0.57 & 0.67 \\ Speed (ms) & 31 & 52 & 40 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained using our dataset. The proposed approach contained three phases, namely traffic light detection, initial light state classification, and arrow type recognition. Table 4 shows the mAPs for each network phase. The mAPs of initial light state classification and arrow type recognition were computed based on the results of traffic light detection computed by the previous subnet. Table 4 depicts that larger errors primarily occurred in the second subnet. Additionally, the mAP of the Green class was the lowest because the arrow light and green light appeared similar from faraway. Table 5 displays the mAPs for all classes. The classes with more training images had higher mAPs. However, data augmentation did not improve the accuracy for the classes with insufficient samples. Finally, several detection results of the unified network are shown in Figure 11. The results show that our approach can obtain the desired performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Detection** & \multicolumn{3}{c}{**State**} & \multicolumn{3}{c}{**Type**} \\ \hline Class & Traffic Light & Red & Yellow & Green & Arrow & Left & Straight & Right \\ mAP & 0.97 & 0.93 & 0.90 & 0.64 & 0.91 & 0.87 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: mAP of each network phase. Figure 10: Traffic lights at the distance of 0 to 15 m could only be taken by the 3.5 mm lens camera. (**a**) A traffic light is at the border of the image, and (**b**) two traffic lights are faraway. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Taffic Light Size** & **0-5** & **5-10** & **10-15** & **15-20** & **20-25** & **25-30** & **30-35** & **35-40** & **40-45** & **45-50** & **50-55** & **55-60** & **60-65** & **65-70** \\ \hline mAP & 0.33 & 0.48 & 0.82 & 0.80 & 0.76 & 0.75 & 0.69 & 0.74 & 0.77 & 0.63 & 0.69 & 0.69 & 0.55 & 0.62 \\ \hline Distance & 0–15 & 15–30 & 30–45 & 45–60 & 60–75 & 75–90 & 90–100 \\ \hline Traffic light size & & & & & & & & & & \\ (3.5 mm) & & & & & & & & & & \\ \hline Traffic light size & & & & & & & & & & \\ (12 mm) & - & 43 & 39 & 29 & 21 & 15 & 15 \\ \hline Distance & 0–15 & 15–30 & 30–45 & 45–60 & 60–75 & 75–90 & 90–100 \\ \hline mAP (3.5 mm) & 0.38 & 0.62 & 0.57 & 0.47 & 0.46 & 0.37 & 0.32 \\ \hline mAP (12 mm) & - & 0.79 & 0.68 & 0.69 & 0.70 & 0.63 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relationship between mAP, traffic light size (height in pixels), and traffic light distance.