## 4 Discussion  
In training the base models, Yolov5m shows the best performance across all metrics, but it has higher computational costs. It can be reduced by reducing the number of layers, model pruning, and quantization. Yolov5n and Yolov5s are more suitable when computational resources and speed are priorities. The modifications improved precision and mAP@50 for all models, with a slight increase in GFLOPs and the number of parameters. Recall remained stable, with minor decreases for Yolov5n and Yolov5m, and an increase for Yolov5s. However, recall can be improved through hyper-parameter tuning, increasing epoch during training, and lowering the confidence threshold during inference. The number of layers also increased, indicating improved performance but at the cost of increased complexity and computational requirements. Despite the performance of the improved models, [32] achieved a higher frame per second (FPS) of 142 compared to Yolov5n with an FPS of 75, as shown in Table 12. However, the FPS of Yolov5n meets  
Figure 10: Detection results on some public datasets: (**a**) Detection results on the GTSRD by TTA; (**b**) detection results on the TT100K test image by mean ensemble; (**c**) detection result showing misclassification and detection of good traffic signs on GTSRD image by improved Yolov5m; (**d**) detection result by improved Yolov5s on CCTSDB2021; (**e**) misdetection by Yolov5s on CCTSDB2021 as rusted traffic sign and correctly detect good traffic sign; (**f**) detection result by Yolov5m on GTSDB dataset.  
the threshold for real-time applications. The proposed mean ensemble model performs best on rusted and good traffic signs, with the highest AP values, but performs least on displaced traffic signs. Test time augmentation significantly improves accuracy but at a higher computational cost and increased inference time. Our proposed models outperform the state-of-the-art models in terms of accuracy and FPS.  
Employing k-fold cross-validation has further highlighted and provided insight into the performance of the models across data splits. In Tables 6 and 7, YOLOv5n has high precision but low recall, which indicates that it misses detection of some traffic signs, while YOLOv5m has high recall but low precision, which leads to false positives. YOLOv5s shows balanced performance but needs improvement in overall accuracy. However, all models have a moderate mAP@50, indicating room for better localization accuracy, and show some variability across folds, suggesting the need for model stability improvements.  
The three techniques we employ to improve the detection accuracy of bad traffic signs have shown that there are few cases where the mAP@50 score has decreased, which suggests that there is room for further optimization.  
In this study, we use YOLOv5 models to improve the detection accuracy of bad traffic signs due to their speed, support for real-time detection, range of sizes suitable for our model, ease of customization, and support for ensemble and test time augmentation (TTA) techniques. While other models such as YOLOv4 and YOLOv8 can achieve good accuracy on the dataset, YOLOv4 offers excellent performance, and YOLOv8 is more accurate than YOLOv5. However, YOLOv5 still provides state-of-the-art accuracy for our model.  
Future research will focus on using different ensemble techniques on modified architectures of other YOLO models and introducing additional classes to the dataset for diversity.