# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 3 Methodology  
Our aim was to evaluate and enhance the performance of object detection in sandy weather. In this section, we will explain in detail the dataset and methods that were used for our experiments. Figure 3 shows a block diagram of our methodology. Our dataset covers challenging sandy weather conditions. Detection in Adverse Weather Nature (DAWN), which was created by the authors of [38], was used as our main source of images. This dataset consists of approximately 1000 images that cover hard weather conditions, such as fog, snow, rain, and sandstorms, see Figure 4. It also covers various types of roads (urban, highways, and freeways). Desert countries have always been subjected to sandstorms, which represent a challenging type of weather for autonomous \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Sandy** & **Foggy** & **Snowy** & **Rainy** \\ \hline
[28] & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
[29] & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline
[30] & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ \hline
[31] & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ \hline
[33] & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline
[34] & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline
[35] & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of adverse weather covered in recent object detection publications. Figure 3: Sequence of our methodology in this paper. vehicles and detection. Due to the fact that none of the previous publications focused on sandy weather, we decided to extract only sandy images from the DAWN dataset as our raw images. Our raw DAWN dataset was split into 258 images (80%) for training and 65 images (20%) for validation, which resulted in a total of 323 images sized 640 \(\times\) 640. We also narrowed our raw experiments to six objects: person, bicycle, car, motorcycle, bus, and truck. Image annotation was provided in the original dataset which contained the class of the object, the corresponding boundaries of x and y, and the width and height of the bounding box (x_center, y_center, width, and height). Figure 5 represents a sample of our labeled images considered as a ground truth reference. Figure 4: The DWAN dataset provides various hard weather conditions such as fog, rain, snow, and sand [38]. Figure 5: Sample of labeled images. Due to the limited GPU resources available and to run our experiments as smoothly as possible, we used the Google cloud-based notebook \"Colab\" as a hosting environment for our experiments, using the machine learning framework \"Pytorch\", with high Graphics Processing Unit (GPU) capabilities. Colab provides a high-performance GPU, such as Tesla T4 for public use. Colab also provides the Compute Unified Device Architecture (CUDA) which speeds up the computation of the CNN process, including convolution, pooling, normalization, and activating layers. Several metrics can be used to measure the performance of the object detection model. In our research we focused on three main metrics: mean average precision (mAP), precision, and recall. mAP is the most commonly used evaluation metric for object detection. It provides a comprehensive measure of the model's accuracy in terms of identifying and localizing objects. It combines precision and recall by calculating the average precision (AP) for each class or category of objects and then taking the mean across all classes. AP measures the quality of object detection by considering both the precision of correctly identified objects and the completeness of the detection. By computing the mAP, we can effectively compare different models and assess their performance in various domains. Precision and recall are also other mandatory metrics for object detection. Precision will show the percentage of retrieved elements that are relevant, while recall is concerned with how many relevant items have been retrieved.