## 2 Related Work
### Studies Related to the Detection of Traffic Signs  
[PERSON] et al. [20] used the YOLOv4 model alongside the K-means algorithm for traffic sign detection. The results showed that the frame rate was 25.3 frames per second (FPS), and the mean average precision (mAP) was 1.9% higher than that of the original YOLOv4. [PERSON] et al. [21] developed an improved K-means algorithm based on YOLOv4-Tiny for traffic sign detection. By generating appropriate threshold values, the model increased the detection accuracy for long-distance and small targets. The authors also proposed an improved non-maximum suppression algorithm to identify the prediction box and avoid deleting the prediction results of different targets. The results showed that the improved algorithm achieved a 5.73% higher mAP at 87 FPS compared to the original YOLOv4-Tiny. [PERSON] and [PERSON] [22] proposed a model that specifically detected small traffic signs by adding a convolutional layer into the YOLOv4 backbone to obtain feature maps with richer information about small objects. In addition, as an effective means of integrating multiscale feature maps, the authors proposed two attention modules--upsampling and downsampling--to assist the network in focusing on useful features. The approach's mAP was 2.2% higher than the original YOLOv4. [PERSON] et al. [23] performed filtering and data augmentation on raw datasets to improve the accuracy of the small target detection of five common detection algorithms--Faster R-CNN, single shot multi-box detector (SSD), RetinaNet, YOLOv3, and YOLOv5--and optimized the network parameters. The experimental results showed that the test accuracies of Faster R-CNN, RetinaNet, YOLOv3, and YOLOv5 were all above 98.2%. Regarding the use of Faster R-CNN in small object detection, [PERSON] et al. [24] developed a sampling method that achieved network optimization by selecting high-quality proposals. Other post-processing solutions have also been proposed for network optimization through resampling. Using Res2 net as the backbone of the proposed network, more distinctive features were obtained, allowing it to outperform the accuracy of the Faster R-CNN by 4%. [PERSON] and [PERSON] [25] compared small target detection through YOLOv4 and YOLOv3 using a dataset of 4000 traffic signs that were manually labeled by the authors. The detection results revealed that YOLOv4 outperformed YOLOv3 by 2.83% in terms of accuracy. [PERSON] et al. [26] proposed an improved YOLOv4 algorithm for small target detection by adding the Inception structure to the regression network. The improved algorithm had a 3.6% higher mAP than the original YOLOv4 network, although the recognition speed decreased marginally. [PERSON] et al. [27] proposed an improved YOLOv5 model for traffic sign recognition by modifying the feature pyramid network (FPN) from three layers to four layers and the path aggregation network (PAN) from two layers to three layers. As a result, the neck of the model additionally generated larger feature maps for identifying small targets, and the mAP was improved by 10% compared to the original YOLOv5. [PERSON] et al. [28] presented a CNN model for traffic sign detection and compared its test results against a German dataset and an Indian dataset. The results revealed that the model achieved a 99.85% accuracy for the former, 91.08% for the latter, and 95.45% for the combined datasets. [PERSON] and [PERSON] [29] employed a Mask R-CNN method that resolved the complete process of detection and recognition through automatic end-to-end learning. The method was able to detect 200 traffic signs. [PERSON] et al. [30] proposed a method that involved image processing for detecting triangular, square, and rectangular traffic signs, thus validating the various shapes of traffic signs. [PERSON] et al. [31] suggested a YOLOv4-based traffic sign detection approach by combining a CNN with a multilayer perceptron architecture, thus strengthening the object detection capabilities of the YOLOv4 network.