# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Perception
#### 3.1.2 Traffic Sign and Light Recognition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline
[46] & BCNN & \begin{tabular}{c} 98.52\% accuracy for \\ low visibility of \\ road signs. \\ \end{tabular} & \
\begin{tabular}{c} Branch-output mechanism \\ enhanced recognition speed \\ and accuracy. \\ \end{tabular} &
\begin{tabular}{c} Implementation from \\ moving platform was \\ not tested. \\ \end{tabular} \\ \hline
[47] & LetNet-5 based CNN & 99.95\% accuracy. & \
\begin{tabular}{c} Lightweight color segmentation \\ before classification, improved \\ processing speed. \\ \end{tabular} &
\begin{tabular}{c} Detected two traffic signs for \\ having different location of \\ proposed region. \\ \end{tabular} \\ \hline
[48] & CNN with Gabor filter & \begin{tabular}{c} Accuracy of 99.75\% \\ in 5.4 ms. \\ \end{tabular} & \
\begin{tabular}{c} Obtained highest accuracy and \\ enhanced the performance \\ of LetNet-5. \\ \end{tabular} &
\begin{tabular}{c} Performance decreased in \\ complicated backgrounds. \\ \end{tabular} \\ \hline
[49] & Weighted multi CNN & \begin{tabular}{c} Highest 99.75\% \\ accuracy in 10 ms. \\ \end{tabular} & \
\begin{tabular}{c} Classification in high-speed \\ driving and outperformed in \\ low-light conditions. \\ \end{tabular} &
\begin{tabular}{c} Struggled to classify in \\ challenging blurry \\ vision condition. \\ \end{tabular} \\ \hline
[50] & SVM & \begin{tabular}{c} Recall score 97\% and \\ 99\% for two cases. \\ \end{tabular} & \
\begin{tabular}{c} Faster detection of prohibitory \\ and danger signs in poor \\ illumination. \\ \end{tabular} &
\begin{tabular}{c} Only applied to red \\ circular signs. \\ \end{tabular} \\ \hline
[51] & YOLO V2 & \begin{tabular}{c} Highest 96.81\% AUC \\ values in 0.017 s. \\ \end{tabular} & \
\begin{tabular}{c} Faster and simple \\ processing pipeline. \\ \end{tabular} &
\begin{tabular}{c} Decrease in performance for \\ small traffic signs. \\ \end{tabular} \\ \hline
[52] & CapsNet & 98.72\% accuracy. & \
\begin{tabular}{c} Resolved loss of max pooling to \\ boost CNN’s performance. \\ \end{tabular} &
\begin{tabular}{c} Did not consider complex \\ lighting condition \\ \end{tabular} \\ \hline
[53] & Fast R-CNN & \begin{tabular}{c} 100\% recall and 92\% \\ average precision. \\ \end{tabular} & \
\begin{tabular}{c} Detected traffic light as well as \\ indicating signs. \\ \end{tabular} &
\begin{tabular}{c} Showed high false positive. \\ \end{tabular} \\ \hline
[54] & DeepTLR & Highest 93.5\% F1 score. & \
\begin{tabular}{c} Did not require position details \\ or temporal principles. \\ \end{tabular} &
\begin{tabular}{c} Lower precision rate. \\ \end{tabular} \\ \hline
[55] & OCF & \begin{tabular}{c} 11.5\% improved AUC \\ value for green light. \\ \end{tabular} & \
\begin{tabular}{c} Better detection in complex \\ scenes with low luminous \\ objects combining. \\ \end{tabular} &
\begin{tabular}{c} Unsatisfied accuracy for red \\ left traffic light identification. \\ \end{tabular} \\ \hline
[56] & YOLO & \begin{tabular}{c} 42.03\% and 49.16\% \\ mAP higher on \\ two datasets. \\ \end{tabular} & \
\begin{tabular}{c} Took upper half of frame to \\ eliminate search area and \\ vehicle taillights. \\ \end{tabular} &
\begin{tabular}{c} Did not deploy in nighttime \\ traffic scene where reflection \\ creates confusion. \\ \end{tabular} \\ \hline
[57] & YOLO & 99\% in 0.06 s. &
\begin{tabular}{c} 3D localization and tracking of \\ traffic lights in large field \\ of view. \\ \end{tabular} & Required more labelling. \\ \end{tabular}
\end{table} 
Table 6: Summary of multiple deep learning methods for traffic sign and light recognition.