# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
## 3 Results
### Ablation Study
#### 3.5.3 Comparative Analysis Under Different Nighttime Scenarios  
In order to comprehensively analyze the performance of the method proposed in this paper, four different nighttime scenarios were selected, as shown in Figure 9.  
As represented in Figure 9a, the recognition accuracy of the YOLOv5 base model was low in the evening long-distance small target scenarios, and only speed limit and prohibition signs could be recognized, with a 58% recognition rate for a speed limit of 30 and an average recognition rate of 83% for prohibition signs. With the introduction of OTA and the CBAM alone, the recognition rate was improved, and an additional indicator sign was recognized; specifically, the recognition rate of a speed limit of 30 was improved to 74% and 31%, the average recognition rate of prohibited signs was improved to 86% and 75%, and the recognition rate of bike lanes was improved to 59% and 63%, respectively. With the simultaneous introduction of the OTA and CBAM mechanisms, the model was able to recognize all signs, with a recognition rate of 76% for a speed limit of 30, 89% for prohibited signs, and 63% for bike lanes. In addition, the model was also able to successfully recognize a traffic sign (speed limit of 50) where the small target was partially obscured by a tree, with a recognition rate of 47%. As represented in Figure 9b, the original YOLOv5 model, as well as the introduction of the CBAM alone, failed to detect the signs in the nighttime occluded scenario. The possibility of wrong detection still exists with the introduction of the OTA loss function alone. After the introduction of the CBAM and OTA, \"NTS-YOLO\" could successfully recognize the prohibited signs with a recognition rate of 51%. As shown in Figure 9c, the recognition rates of YOLOv5, YOLOv5 + OTA, and YOLOv5 + CBAM were 88%, 89%, and 89%, respectively, for the warning sign \"let\" in the nighttime blurred scene. After the introduction of OTA and the CBAM, the recognition rate of \"NTS-YOLO\" reached 92%, which was an improvement of four to five percentage points. As represented in Figure 9d, in the nighttime rainy blur scenario, YOLOv5 and YOLOv5 + CBAM did not detect the sign, and YOLOv5 + OTA recognized the prohibited sign with a recognition rate of 51%. With the introduction of OTA and the CBAM, \"NTS-YOLO\" was able to successfully recognize two traffic signs with recognition rates of 91% and 60%, respectively. A comprehensive analysis of the experimental results showed that the improvements introduced by adding the CBAM attention mechanism and the OTA loss function had significant advantages in recognizing traffic signs under complex conditions such as dusk, occlusion, and blur. The CBAM mechanism helps the model to more accurately capture key features, thereby improving the accuracy of object detection. Meanwhile, OTA optimizes the learning process by adjusting the loss function, further enhancing the model's recognition performance. The combination of these two mechanisms provides the model with more precise object detection capabilities, effectively improving the overall recognition performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Recall** & **mAP-0.5** & **mAP-0.5:0.95** & **Speed** & \multirow{2}{*}{**GPU**} \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(it/s)** & \\ \hline Faster R-CNN & 81.23 & 83.45 & 87.12 & 65.30 & 3.12 & 6.8 \\ YOLOv3 & 83.15 & 85.21 & 89.50 & 68.42 & 2.85 & 5.8 \\ YOLOv6 & 86.50 & 87.90 & 91.40 & 70.60 & 2.65 & 5.6 \\ YOLOv7 & 88.20 & 88.50 & 92.10 & 71.85 & 2.50 & 5.4 \\ YOLOX & 87.80 & 88.30 & 92.05 & 71.40 & 2.47 & 5.3 \\ ETSR-YOLO & 87.85 & 88.01 & 92.72 & 72.21 & 2.45 & 5.7 \\ YOLOv5 & 85.98 & 88.71 & 92.39 & 72.40 & 2.38 & 5.2 \\ NTS-YOLO & 90.59 & 88.81 & 93.27 & 73.60 & 2.57 & 5.7 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of mainstream object detection models. Figure 9: Comparison of results in different scenarios.