# Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis
## 2 Materials and Methods  
### Traffic Sign Recognition with You Only Look Once (Yolo) V3  
Based on [29], this research work combines Adaboost, and Yolo V2 approaches for traffic sign studies. The system uses real traffic signs collected in the center of Kaohsiung, a large city in southern Taiwan. Additional research on traffic signs, particularly in Taiwan, is presented in [30]. This work tracks traffic signs from video recordings using its proposed program for obtaining the traffic signs image. CNN validates the precision of the generated dataset. In [31], focus on Taiwan's stop sign detection and recognition. They conduct some experiments with a different setting and analyze the importance of anchor calculation using k-means and original Yolo V3 for Taiwan stop sign detection and recognition. Their experiment proved that anchor recalculation based on our dataset is very important. [PERSON] et al. [28] investigates the state-of-the-art of various object detection systems including Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3 combined with spatial pyramid pooling (SPP). Their research adopts the concept of SPP to improve the backbone network of Yolo V3, Resnet 50, Densenet, and Tiny YoloV3. Hence, their experiment findings show that Yolo V3 SPP strikes the best total BFLOPS (65.69), and _mAP_ (98.88%). The highest average accuracy is Yolo V3 SPP at 99%, followed by Densenet SPP at 87%, Resnet 50 SPP at 70%, and Tiny Yolo V3 SPP at 50%. Hence, SPP can improve the performance of all models in the experiment. Other research studied various weights presented by the darknet framework, including the best weight, the final weight, and the last weight [31]. They conduct and analyze the comparative experiment of Yolo V3 and Yolo V3 SPP with different weights. Experimental results show that the mean average precision (_mAP_) of Yolo V3 SPP is better than other models. Based on the previous research work we found that nobody focused on the significant of scale parameter of Yolo in the configuration file. In our research will concentrate more on the importance of scale parameters in the Yolo V3 and Yolo V3 SPP configuration file. Yolo V3 was introduced the first time by [PERSON] et al. [32; 33] in 2016. A single neural network interprets the entire picture. Yolo V3 separated the image into grid cells and provides boundary boxes and possibilities for each grid cell [34]. Yolo V3 makes a prediction using multiscale fusion. The 416 \(\times\) 416 input image size is integrated with three scales using up-sample and FPN fusion [35]. The three scales obtained are 13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52, respectively [36]. Yolo V3 consists of 53 layers with deep characteristics and was built on Darknet-53. Yolo V3 has demonstrated better than ResNet-101, ResNet-152, or Darknet-19 [33]. Figure 1 exhibits the construction of Darknet-53. The input image is divided by the Yolo V3 algorithm into S\(\times\)S grids. If the central point of the ground reality of the object decreases within the required grid, the grid can define the target. Each grid outputs B bounding prediction boxes, including bounding box location data that consist of coordinates of the middle point (\(x\), \(y\)), width (\(w\)), height (\(h\)), and confidence prediction. The Yolo loss function of the boundary box consists of four sections [37], and the formula could be seen on Equation (1) [38; 39; 40]. \[Loss=Coord\_Err+\ BBBox\_Err+Category\_Err+Conf\_Err \tag{1}\]  
Further, \(Coord\_Err\) is the loss of predicted central coordinate, and \(BBox\_Err\) is the loss of width and height of the prediction bounding box. Next, \(Category\_Err\) is the loss of the predicted category, and \(Conf\_Err\) is the loss of the predicted confidence. The process of measurement is shown in Equations (2)-(5). \[Coord\_Err=\ \lambda_{coord}\sum_{i=0}^{s^{2}}\sum_{j=0}^{B}t_{ij}^{obj} \Big{[}\left(x_{i}-\hat{x}_{i}\right)^{2}+\left(y_{i}-\hat{y}_{i}\right)^{2} \Big{]} \tag{2}\]  
Figure 1: Darknet-53 Structure.