## 2 Related Work
### Studies Related to the Detection of Traffic Signs
Then, the authors added a same-level connection within the PAN to enhance the network's acquisition of feature information. The experimental results revealed that the improved YOLOv4 algorithm increased the mAP from 72.95% to 78.84%. [PERSON] and [PERSON] [32] presented an improved YOLOv4-based algorithm called TSnet, which resulted from restructuring and adjusting the network structure of the YOLOv4. To enhance feature extraction, the authors used DenseNet to replace the residual unit in the backbone network with a dense connection unit. The experimental results indicated that TSnet had a 3.83% higher mAP than YOLOv4. [PERSON] et al. [33] proposed an improved YOLOv4 model for traffic sign recognition derived from adding a cross-layer connection to the YOLOv4 network and adjusting the weight of the transferred feature map, thus enhancing the network's feature extraction capabilities. The method was able to detect a wider range of traffic signs, with a 1.03% mAP improvement over the original YOLOv4. [PERSON] and [PERSON] [34] developed an improved Canny edge detection method and analyzed its suitability for traffic sign edge detection under various external interference factors. In addition, to address the insufficiency of traffic sign data, [PERSON] et al. [35] proposed a data augmentation method in which synthetic images were created using least-squares generative adversarial networks (LSGANs), thus enlarging the original image dataset. After mixing the original images with the synthesized LSGANs-generated images, the recognition performance, as tested by YOLOv3 and YOLOv4, had improved, with the former achieving an 84.9% accuracy and the latter an 89.33% accuracy. Contrastingly, some researchers have focused on network architecture designs with faster detection speeds. [PERSON] and [PERSON] [36] presented a lightweight real-time traffic sign detection model that augmented the information sharing between all levels. An optimized network was used to improve the speed of feature extraction and reduce the computational time and hardware requirements. The results showed that the FPS of the new model was 31 units higher than the YOLOv4. [PERSON] et al. [37] similarly enhanced the detection speed of YOLOv4 by improving the YOLOv4 backbone extraction network through separable convolutions. The difference between the mAP of the improved and original models was only 0.88%, but the improved model's detection speed was nearly three times higher. [PERSON] et al. [38] presented a lightweight traffic sign recognition algorithm based on cascaded CNN. Compared to the YOLOv2-Tiny algorithm, the new algorithm had a 55% lower computational complexity and 32% shorter central processing unit (CPU) computational time, while maintaining the mAP at a similar level. [PERSON] et al. [39] proposed a cross-stage partial YOLOv4 model that improved the original YOLOv4 through the use of CNNs. The improved model had a mAP of 79.77% at 29 FPS, which was extremely beneficial for identifying labels in continuously detected videos. [PERSON] et al. [40] proposed an extended LeNet-5 CNN model, using the Gabor-based kernel followed by the normal convolutional kernel after the pooling layer. The hue and saturation value color space features had faster detection speeds and fewer impacts from illumination.