# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
## 3 Results
### Ablation Study
#### 3.5.1 Performance Analysis of Ablation Study  
In order to verify the effectiveness of the improved nighttime traffic sign recognition algorithm proposed in this paper, ablation experiments were conducted. Firstly, we compared the addition of the CBAM attention mechanism and the OTA loss function alone, respectively, and then applied them both to the YOLOv5 model and observed the performance in various cases. The results are shown in Table 4. In this paper, mAP-0.5 was chosen as the evaluation index, which could comprehensively assess the performance of the model in the target detection task. In the experiments, we first used the original YOLOv5 model for the nighttime traffic sign recognition task and obtained a benchmark performance with an mAP-0.5 metric of 92.39%. Next, we added the CBAM attention mechanism and OTA loss function to the YOLOv5 model and conducted the experiments separately. The results showed that the addition of the CBAM attention mechanism slightly improved the model's mAP-0.5 score to 92.44%, an increase of 0.056%. This indicates that the CBAM mechanism helped the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **CBAM** & **OTA** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & \
\begin{tabular}{c} **mAP-0.5** \\ **(\%)** \\ \end{tabular} & **
\begin{tabular}{c} **mAP-0.5:0.95** \\ **(\%)** \\ \end{tabular} & **Speed (s)** & **GPU** \\ \hline YOLOv5 & - & - & 85.98 & 88.71 & 92.39 & 72.40 & 2.38 & 5.2 \\ YOLOv5 + CBAM & \(\surd\) & - & 89.34 & 87.31 & 92.44 & 72.59 & 2.58 & 5.5 \\ YOLOv5 + OTA & - & \(\surd\) & 87.45 & 87.31 & 92.43 & 72.69 & 2.58 & 5.4 \\ NTS-YOLO & \(\surd\) & \(\surd\) & 90.59 & 88.81 & 93.27 & 73.60 & 2.57 & 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results of different methods. model to better focus on the key regions of the image, such as the edges and shapes of the signs, which improved the performance of the model. On the other hand, when the OTA loss function was introduced, the mAP-0.5 metric of the model was further improved to 92.43%, an increase of 0.052%. This indicates that the optimized loss function could effectively optimize the training process of the model and improve the accuracy of target detection. The OTA loss function achieved a better match by minimizing the cost between the predicted bounding box and the true bounding box, allowing the model to more accurately predict the target location and the bounding box, thus improving the robustness and stability of the model. And when the CBAM and OTA were simultaneously applied to the YOLOv5 model, it could be observed that the mAP-0.5 score was improved to 93.27%, which was an increase of 0.88% compared to the original model. However, it is worth noting that the introduction of the CBAM and OTA increased the computational complexity of the model, resulting in a reduction in the processing speed from 2.38 it/s to 2.58 it/s, approximately a 7.98% decrease. This speed reduction is mainly attributed to the attention computation in the CBAM and the dynamic matching optimization in the OTA loss. While these modules improve the model's adaptability to small targets and complex scenarios, they also add an extra computational burden and memory usage. Specifically, under the 1280 \(\times\) 1280 resolution setting, the NTS-YOLO model occupied approximately 5.7 GB of GPU memory, which was 0.5 GB more than the original YOLOv5. This increase in memory usage was primarily due to the attention computation in CBAM and the matching optimization in the OTA loss. In terms of GPU utilization, the NTS-YOLO model maintained a utilization rate of approximately 72%, compared to 65% for YOLOv5, demonstrating higher resource usage. This indicates that the improved model makes more efficient use of hardware computing resources while achieving high-performance operation in the current hardware environment.