# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 6 Augmented DAWN with YOLOv5 and YOLOv7 Results  
The experimental scenario for the augmented DAWN was executed within the Google Colab environment, harnessing the computational power of a its GPU. The machine learning framework PyTorch served as the cornerstone of our methodology, enabling efficient model training and evaluation. The experimentation process lasted for 64 epochs, with each epoch comprising 16 batches of images. The scenario detection task focused on three classes, car, person, and bicycle, reflecting common entities encountered in real-world scenarios. We used YOLOv7 as our base architecture due to its outstanding performance compared to other object detection models. YOLOv7 is considered as a state-of-the-art architecture. It outperformed all known object detectors in speed in the range of 5 FPS to 160 FPS and had the highest accuracy of 56.8% AP among all the known real-time object detectors, with 30 FPS or higher on GPU V100. After running the experiment of YOLOv7, SiLU achieved the best overall best performance amongst all the activation functions in the augmented dataset. We achieved an mAP of approximately 94% using YOLOv7 and the SiLU function to detect the three classes. This is the highest score compared with the other adverse weather object detection publications that are mentioned in Table 1. The chart showing precision, recall, and mAP can be seen in Figure 17. The top left chart shows the precision result, with a score of 92%, while the top right chart shows the recall result reaching 85%. The bottom chart shows the resulting mAP which reached 94%. The F1 score is presented in Figure 18. The F1 score combines precision and recall into a single metric, providing us with a balanced assessment of our results. Our remarkably high F1 score is a strong indicator of the model's ability to balance between precise object detection in sandy weather (limited false positives) and capture most of the actual objects in the scene (limited false negatives). This is particularly important in real-time sandy weather tasks where both accuracy and completeness matter. The predicted labels of the experiments are shown in Figure 19.  
Figure 17: YOLOv7 results of our experiment. Choosing the right activation function is crucial for the model's performance, as can be seen in Table 5. YOLOv7 with SiLU outperformed YOLOv7 with LeakyReLU with a significant performance. Using SiLU led to an increase in the mAP of the model by approximately 18 percentage points. This score is approximately 12 percentage points higher than that of the closest YOLOv5 model, which is YOLOv5l with SiLU. Regarding car class detection, all of the models, with their various activation functions, reached an mAP above 82%. The person class was quite similar to the car class, where the lowest achieved mAP was 83% using YOLOv5s with ReLU. Table 6 shows a summary of the  
Figure 19: YOLOv7 predicted objects using SiLU. Figure 18: YOLOv7 F1 score. augmented DAWN results. From the activation functions point of view, SiLU achieved the highest mean average precision in all the scenarios using both the YOLOv5 and YOLOv7 models. As can be observed, relying on one activation function for all scenarios might lead to a drawback in the model's performance. If we compare the performance of the two YOLO versions with the original DAWN dataset and the augmented DAWN dataset, we can see that the highest mAP score was recorded for the augmented dataset using SiLU. However, in the original dataset, LeakyReLU had the highest mAP score. The results show that, in terms of mAP, there is no single model or single activation function that yields the highest performance in all scenarios. The selection of the model activation function is very subjective. It is based on the architecture design and the problem characteristics. In a paper published by [PERSON] et al. [41], the authors stated, \"There is a need for novel deep learning architectures and solutions that have adequate capacity for handling object detection under diverse conditions\".