# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 1 Introduction
However, we are still lacking a reliable dataset that could enhance research in this domain. In fact, there are several key obstacles that AVs may face in sandy weather environments:  
* As a common occurrence in deserts and coastal regions, dust and sand particles in the air can severely impair visibility and reduce the accuracy of object detection algorithms. * Occlusion is where objects are covered by other objects, making perception very difficult for the deep learning model. Occlusion makes it difficult for detection algorithms to correctly determine object boundaries and characteristics. * Changes in lighting during storms can affect the performance of cameras and sensors used in object detection. * Road collisions with wildlife animals are a significant risk for vehicles, and it has been reported that wildlife animals are causing risk for vehicles [12]. * There is a lack of sandy weather datasets, with most of the public datasets focusing on other types of weathers (foggy, snowy, and rainy). Figure 1 shows some object detection challenges in sandy weather. We can clearly see that sandstorms and sandy environments can drastically change scene lighting and reduce visibility, making it difficult for sensors to accurately perceive objects. All of the previously mentioned limitations are challenges in sandy weather. AVs heavily rely on object detection systems to perceive and interpret their environment. The challenges posed by sandy weather can significantly impact their safety and reliability, which are two critical aspects in the development and deployment of AVs. Overcoming these challenges will play a vital part in the transition toward a future with safer and more reliable autonomous transportation systems. In this paper, we tackle two of the challenges that arise in sandy weather. The first is the challenge of lacking datasets by providing an augmented dataset, and the second is the visibility of the scene and to evaluate the performance of YOLOv5 and YOLOv7 in such weather. Metrics such as precision, recall, and mAP will be our bases for evaluation. Figure 2 shows the scope of our paper which focuses on detecting objects using the camera (as a sensor) in sandy weather. Figure 1: Some sandy weather challenges. Figures (**a,b**) demonstrate changes in lighting during a sandstorm. Figures (**c,d**) illustrate the degradation of visibility during a sandstorm. In figures (**e,f**), the road boundaries are obscured by sand. Figure 2: Scope of our paper. The main contributions of our work are as follows:  
* We extended the Detection in Adverse Weather Nature (DAWN) dataset and added augmented images. The sandy weather dataset was expanded from 323 images to 1137 images. The augmentations that were used include saturation, brightness, darkness, blur, noise, exposure, hue, and gray scale. * We used object detection models (YOLOv5 and YOLOv7) as base architectures for detecting three classes of objects (car/vehicle, person, and bicycle) in sandy weather. * We evaluated different activation functions for detecting objects in sandy weather. The following section will shed light on activation functions and recent related work on object detection in adverse weather conditions. In the next section, we will provide a detailed explanation of the dataset, methodology, and evaluation metrics used in our experiments. Subsequently, we will present the results of the experiments conducted using the original DAWN dataset, followed by those obtained using the augmented DAWN dataset. The final part of this paper will focus on discussing the outcomes of the experiments and their evaluation, before concisely summarizing the findings.