# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 1 Introduction  
Recognizing objects in the surrounding area is a crucial milestone in the development of autonomous vehicles (AVs). To eliminate the risk of accidents and to navigate safely in various environmental conditions, AVs are required to scan the scene through their equipped sensors, localize objects, classify objects, and finally, take action (in the form of a response) based on the detected data that they have collected. The above process is very challenging when it comes to detection in adverse weather conditions such as rain, fog, and sand. Each of these weather conditions presents unique challenges that can affect the performance of object detection systems. Sand, rain, and snow can obscure objects, reduce visibility, and create reflections on surfaces, making it difficult for sensors to accurately detect and recognize objects. Fog can significantly limit visibility and cause signal attenuation, further degrading the accuracy of object detection algorithms. Harsh sunlight can lead to glare and cast strong shadows, making it challenging to distinguish objects from their surroundings. One significant milestone in the development of AVs and object detection is the transition from the traditional computer vision methods to deep learning-based approaches, particularly with the use of Convolutional Neural Networks (CNNs). This milestone represents a shift towards more powerful and accurate object detection models in various scenarios and weather conditions. The milestone was marked by the emergence oflandmark algorithms such as Regions with Convolutional Neural Networks (R-CNNs) [1] and its subsequent variants, including Fast Region-based Convolutional Network (Fast R-CNN) and Faster Region-based Convolutional Network (Faster R-CNN) [2,3]. These algorithms, which are called two stage models, introduced the concept of region proposal methods, in which potential object regions are first identified and then classified using CNNs. This breakthrough significantly improved object detection accuracy in various weather conditions and laid the foundation for subsequent advancements. Another significant milestone was reached with the development of the You Only Look Once (YOLO) algorithm. These milestones introduced the concept of real-time object detection performed directly on a single network pass (one stage), eliminating the need for region proposal methods. Additionally, they marked a significant improvement in computational efficiency while maintaining competitive detection performance. These advancements in object detection have paved the way for more recent milestones, such as the appearance of anchor-free approaches (e.g., CenterNet and EfficientDet [4,5]) and the incorporation of transformers (e.g., DETR [6]). Through these milestones, researchers continue to push the boundaries of object detection, improving accuracy, speed, and robustness for a wide range of applications in various domains. The growth of sandstorms [7,8] reflects the importance of developing robust object detection algorithms and sensor technologies that can adapt to the challenges of adverse weather conditions. Dense airborne particles could be misinterpreted as an object ahead of the vehicle and possibly result in a false positive action from the AV [9]. Drastic changes in lighting with sudden darkness during sandstorms may lead to poor image quality and make it challenging for the vehicle's perception system to accurately recognize and track objects. Road collisions with animals are also a serious issue in sandy environments, such as the Middle East, where animals, specifically camels, can often be seen walking alongside or crossing roads, causing major problems for road safety. The authorities in Middle East countries are continuously working to find solutions to this problem, but it remains a significant challenge for road safety and AVs. From a computing standpoint, great efforts are being made to provide road animal datasets and to apply detection in this domain, as in [10,11].