# Traffic Sign Detection Based on the Improved YOLOv5
## 3 Experimental Validation and Result Analysis
### Quantitative Analysis  
This paper uses the original algorithm and conducts improved algorithm experiments and ablation experiments on the TT100K dataset to test the improvement in model performance from adding the CBAM attention mechanism, improved activation function, and improved loss function. Different improvements are tested in the same hardware environment. Figure 7 shows the mAP before and after the enhancement, indicating a significant improvement in the mAP of YOLOv5. To examine the impact of SIoU, ACONC, and CBAM on the road sign recognition algorithm, a series of ablation experiments were designed and conducted on the TT100K dataset under consistent conditions: (1) YOLOv5 original model; (2) YOLOv5 original model + SIoU; (3) YOLOv5 original model + ACONC; (4) YOLOv5 original model + CBAM; and (5) YOLOv5 original model + SIoU + ACONC + CBAM. The experimental performance of the models is analyzed and compared in Table 1.  
Figure 7: Comparison of model mAP before and after the improvement. When only the loss function is improved, the parameters and GFLOPs do not change, indicating that the number of layers in the network does not change when the loss function is changed; the mAP increases from 75.7% to 79.7%, an increase of 4.0 percentage points. When only the activation function is improved, as the parameters increase, the GFLOPs increase, and the hardware computation efficiency is also higher; the mAP increases from 75.7% to 78.7%, an increase of 3.0%. When only the feature enhancement model is fused, the parameters and GFLOPs increase slightly, the hardware computing efficiency is also increased slightly, and the mAP increases from 75.7% to 78.6%, an increase of 2.9%. When all three are added to the original algorithm, the number of parameters and GFLOPs of the network increase, and the hardware computing efficiency also increases. The improved YOLOv5 precision rate increases from 73.2% to 81.9%, an increase of 8.7%; the recall rate increases from 74.2% to 77.2%, an increase of 3.0%, and the mAP increases from 75.7% to 81.9%, an increase of 6.2%. After calculation, the FPS also increases from 26.88 to 30.42 frames per second. In general, the augmented model's detection speed and accuracy effectively improved. This paper compares the improved algorithm to other popular algorithms, such as SSD300, Faster R-CNN, TT100K by Zhu [23], YOLOv3, and YOLOv4 [24,25]. The learning rate of all models is set to 0.001; the input size, epoch, and batch size are shown in Table 2 below. The anchor boxes are set to the original data, and the YOLOv5 model adopts the same parameter-setting strategy as us for training and adjusting parameters. The input size is 640 \(\times\) 640, the batch size is 16, and the epoch is 150. The comparison results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **SIoU** & **ACONC** & **CBAM** & **Parameters** & **GFLOPs** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** \\ \hline YOLOv5 & & & & 7,066,239 & 16.4 & 73.2 & 74.2 & 75.7 \\ A & \(\surd\) & & & 7,066,239 & 16.4 & 79.3 & 75.8 & 79.7 \\ B & & \(\surd\) & & 7,469,903 & 16.7 & 77.2 & 74.4 & 78.7 \\ C & & & \(\surd\) & 7,110,151 & 16.5 & 78.0 & 73.5 & 78.6 \\ Ours & \(\surd\) & \(\surd\) & \(\surd\) & 7,513,815 & 16.8 & 81.9 & 77.2 & 81.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Input Size** & **Learning Rate** & **Epoch** & **Batch Size** \\ \hline YOLOv5 & 640 \(\times\) 640 & 0.001 & 150 & 16 \\ SSD300 & 300 \(\times\) 300 & 0.001 & 300 & 32 \\ Faster R-CNN & 416 \(\times\) 416 & 0.001 & 2500 & 20 \\ Zhu & - & - & - & - \\ YOLOv3 & 416 \(\times\) 416 & 0.001 & 200 & 8 \\ YOLOv4 & 416 \(\times\) 416 & 0.001 & 200 & 8 \\
[25] & 416 \(\times\) 416 & 0.001 & 200 & 8 \\ Ours & 640 \(\times\) 640 & 0.001 & 150 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Partial training parameters of the model.