# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 5 Augmented DAWN Dataset  
Because the DAWN dataset has a limited number of sandy weather images, we expanded the dataset by creating new augmented images. Data augmentation is a common technique that artificially expands the dataset by generating additional training samples from the existing data. Several researchers have used data augmentation for object detection, e.g., refs. [39; 40]. Data augmentation can involve various operations, including image scaling, rotation, cropping, flipping, colorization, and the addition of noise or blur. Figure 8: Detection result for YOLOv5s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Function** & **Image Size** & **mAP** & **Precision** & **Recall** \\ \hline YOLO5s & SiLU & 640 & 80\% & 79\% & 76\% \\ \hline YOLO5s & ReLU & 640 & 75\% & 59\% & 55\% \\ \hline YOLO5s & LeakyReLU & 640 & 71\% & 10\% & 11\% \\ \hline YOLO5m & SiLU & 640 & 85\% & 73\% & 69\% \\ \hline YOLO5m & ReLU & 640 & 82\% & 80\% & 70\% \\ \hline YOLO5m & LeakyReLU & 640 & 88\% & 97\% & 12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of results using original DAWN dataset and YOLOv5. Additionally, object-specific augmentations can be performed by applying geometric transformations, such as scaling, rotation, and translation, to the bounding boxes that enclose the objects of interest. Augmenting the object detection dataset can benefit the model in several ways:  
1. Increasing the diversity and variability of the training data, which can help to generalize the model to unrepresented scenarios. 2. Improving the model's robustness against various factors that may affect the object's appearance or shape, such as different lighting conditions, occlusions, or viewpoint changes. 3. Balancing the class distribution in the dataset by oversampling the minority classes or undersampling the majority ones. 4. Reducing overfitting by introducing regularization and noise to the training data. For the DAWN dataset, we performed several augmentations to expand our dataset. The following are the augmentations we performed:  
5.0.1. Blur  
Blur is used to introduce out-of-focus effects into images. For our augmented data, we used Gaussian blur with up to 1.25 px. 5.0.2. Saturation  
Saturation alters the intensity of colors in the image. When we saturate an image, we basically multiply the pixel values by a random factor within a certain range. Increasing the saturation value of an image can make the colors more vibrant and vivid, while decreasing it can make the colors more subdued and muted. We augmented the saturation of our dataset by approximately 50%. 5.0.3. Brightness  
By randomly increasing the brightness of an image, we exposed our model to a wider range of lighting conditions, making it more robust to changes in illumination. We augmented the images and made them approximately 20% brighter. 5.0.4. Darkness  
This is the opposite of the previous operation, where darkness applied to an image. This augmentation can be helpful for simulating scenarios where the lighting conditions are poor, as in the night time, or in bright lighting conditions, such as bright sunlight. 5.0.5. Noise  
We also added synthetic noise to maximize our dataset. This kind of augmentation makes the model more robust to noise and improves its ability to handle new data or scenarios. 5.0.6. Exposure  
Additionally, we artificially modified the exposure level of the images, setting it in the range of 15% to \(-\)15%. 5.0.7. Hue  
Our augmentations also included hue, which is a color-based image augmentation technique that changes the hue or color tone of an image without affecting its brightness or saturation. 5.0.8. Grayscale  
Finally, we added grayscale augmentation that converts an image into grayscale. This technique is commonly used to increase the contrast of an image and enhance its details. Table 4 shows our augmentation setting values and their impacts on images. After performing the augmentations, the number of DAWN sandy images increased from only  
Figure 11: Saturation augmentation.