# 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

Table 5: Summary of multiple deep learning methods for vehicle detection. On the other hand, the parallel architecture weighted multi-convolutional neural network took 4.6 ms more to process but still achieved constant high efficiency, scoring 99.75% in GTSDB and 99.59% accuracy in the GTSRB dataset, where low and complex lighted scenarios were also considered. Despite occasional accuracy drops for blurry vision, this method could be one of the most suitable approaches for AVS [49]. To detect traffic signs, [PERSON] et al. suggested a red bitmap extraction and SVM-based method where the detected images were color-segmented and afterwards the shape detection of ROI (ROI) was carried out on the basis of rim detail [50]. The methodology scored recall values of 97% and 99% for danger and prohibitor for the GTSDB dataset, respectively. This technique obtained good detection accuracy, but the major limitation was that this method was only applied to red circular signs. [PERSON] et al. demonstrated a modified YOLO V2 algorithm to develop an improved Chinese traffic sign detection system, as well as constructing the database [51]. In order to create a single convolutional network, they used several 1 \(\times\) 1 convolutional layers for the intermediary and fewer convolutional layers for the top layers. Fine grid was also used to separate images with the goal of identifying small-sized road signs. Their technique was found to be the outcome of the CCTSDB and GTSDB databases, where AUC values for mandatory and danger signs were 96.81%, 94.02% and 96.12% in 0.017 s.  
Another approach applied CapsNet, which resolved the major limitation of CNN, that is, loss of max pooling layer retaining spatial relations [52]. The approach obtained 98.72% accuracy for recognizing traffic light with shape. It can be a useful method-based approach for AVS's traffic sign-recognition methods. Furthermore, a unified deep convolutional traffic light-identification feature for automated driving systems was proposed by [PERSON] et al., based on Faster R-CNN that was suitable for detection of traffic lights, and the recognition and classification of types or states [53]. They achieved 92% average precision applying on a large-scale dataset named DriverU traffic light. When the width was greater than 8 px and smaller than these, it scored 93% for average precision. However, there were still limitations for suitable number of false positives which can be reduced by applying RNN or an integrated approach. DeepTLR was the real-time vision-dependent, in-depth and deeply convoluted traffic light-identification and classification system that did not require position details or temporal principles; these were proposed by [PERSON] et al. [54]. On the basis of the single-frame assessment of a challenging collection of urban scenes, the authors presented noteworthy outcomes, showing that in regular images, DeepTLR achieves frame rates of up to 33 Hz. DeepTLR also ran at frame rates of up to 33 Hz. DeepTLR also ran at frame rates of 13 Hz on images with a resolution of 1280 \(\times\) 960 pixels. The capacity for more transport lights was high in the architecture, scoring 93.5% F1 score for 1280 \(\times\) 960 resolution and 88.1% F1 score for 640 \(\times\) 480 in 80 ms and 28.88 ms.  
[PERSON] et al. developed a framework of robust traffic-light recognition with fusion detection in complex scenes [55]. To increase accuracy for each traffic light type and the creation of a fusion detective framework, a set of enhanced methods was adopted based on an optimized channel function (OCF) system after using aspect ratio, field, location and traffic lights background as prior knowledge to minimize computational redundancy and create a task model for the identification of traffic light. Furthermore, they utilized the detection knowledge of the previous system to change the original ideas, which further increased the accuracy.