# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 2 Background and Related Work
### Related Work  
Object detection in adverse weather conditions is a challenging task due to the degradation of the image quality and the loss of visual features caused by weather phenomena such as rain, fog, snow, and haze. These weather conditions affect the performance of detection by reducing the contrast in the scene, decreasing the visibility of the object, and making it difficult to distinguish between objects and the surrounding elements. In [27], the authors used YOLOv4 with spatial pyramid pooling (SPP-NET) layers to detect cars in severe weather, including haze, dust, snow, and rain. Several augmentation techniques have been used to maximize the DAWN dataset such as hue, saturation, exposure, brightness, darkness, blur, and noise. The authors achieved an mAP of 81%. Unfortunately, this paper focused on detecting only one kind of object, which was car class. Despite applyingaugmentation techniques, only two types of augmentations (hue and saturation) were added in sandy weather. In [28], the authors also aimed to detect vehicles in severe weather. YOLOv4 was proposed, with an anchor-free and decoupled head. The authors achieved an mAP of 60.3%. Similar to the previous paper, only one class was used, which would limit the model's performance in reality. The authors of [29] proposed a model for detecting and extracting high-precision vehicle motion data under various weather conditions. The proposed tracking model, called SORT++, provides data such as the vehicle trajectory, vehicle speed, and vehicle yaw angle. Although that paper presented a new dataset called Multi-Weather Vehicle Detection (MWVD), this dataset, like many others, lacks sandy weather. Only rainy and snowy are covered, with a lack of other global weathers. Another paper [30] introduced an image enhancement framework called Image-Adaptive YOLO (IA-YOLO). The input image passes through a pre-processing phase in the Differentiable Image Processing (DIP) filter and is then fed into the YOLOv3 model. Through this framework, every targeted image can be enhanced for better quality and subsequent accurate object detection performance. This framework is promising; however, more challenging weather conditions and scenarios need to be evaluated, instead of using only foggy images. The Dual-Subnet Network (DSNet) was introduced in [PERSON] et al. 's [31] paper. The developed network consists of two sub-networks, which are the detection subnet and restoration subnet. This paper focused on foggy weather, and the authors achieved an approximately 51% mAP on their composed dataset and approximately 42% on the Foggy Driving dataset [32]. In [33], the authors collected a Street-Level Video dataset that provided 11 classes including pedestrians, vehicles, and traffic lights. YOLOv5 was used, and an mAP of 72.3% was achieved for detecting vehicles. Unfortunately, one kind of adverse weather was covered, specifically rain, while other challenging weather conditions were out of the paper's scope. In [34], the authors addressed challenging weather using images that were taken with drones. Considering the previously mentioned related works, we can clearly see a gap and a bias of research papers against sandy weather. From our point of view, one obvious reason for not including sandy weather in object detection experiments is the lack of sandy datasets, which we aimed to overcome by creating an augmented DAWN dataset. Table 1 shows a summary of recent adverse weather publications. Table 2 shows a sample of recent publications on object detection and the kinds of weather they address. From the samples mentioned in the table, we can clearly see that most of the publications focus on foggy and rainy weathers, omitting other kinds of weather