## 2 Dataset Description and Applied Algorithms  
### Dataset Description  
The DAWN and RTTS datasets with similar object labeling types are the preferred datasets in the literature. The DAWN dataset has data content that includes only road images and all adverse weather conditions. RTTS, on the other hand, has data content that includes only foggy and normal images. These two datasets are used to compare the performance of the applied methods.  
DAWN: Vehicle Detection in Adverse Weather Nature Dataset: Few datasets address adverse weather conditions through a combination of synthetic weather and real-world images [18]. To overcome this shortcoming, [PERSON] and [PERSON] introduced a dataset called DAWN, which includes various AWCs to be used in vehicle detection in 2020 [18]. The DAWN dataset is a collection of high-resolution images and videos recorded under different weather conditions [18]. Roads in urban settings and rural settings, intersections, pedestrian crossings, and traffic signs are included in the dataset. Object detection, classification, and tracking can be performed using the dataset's labeled samples. Thanks to its wide scope and diversity, the DAWN dataset offers researchers and developers the opportunity to test and improve autonomous driving algorithms. This dataset is widely used for training and performance evaluation of deep learning models. In this study, we utilize the DAWN dataset for the detection of vehicles in AWCs. DAWN is a dataset specially created for AWCs. In this research, the version of the DAWN dataset customized for YOLO is used, which was obtained from the Roboflow website [32]. Figure 1 shows the labeling rates of the objects in the DAWN dataset. The labeled objects include people, bicycles, cars, motorcycles, buses, and trucks.  
This dataset is categorized as 2053 training, 197 validation, and 99 test data to be trained using the deep learning method. In this dataset, fog, snow, rain, and sandstorm images are randomly selected. 197 validation images are used to help determine the most efficient hyperparameters. A set of 99 test images that never entered training is used to evaluate the effectiveness of object detection. Figure 2 shows some images of the DAWN dataset.  
Figure 1: Sample numbers of labeled objects in the DAWN dataset.  
**Real-Time Transportation System (RTTS) Dataset:** RTTS is the largest annotated detection dataset in hazy conditions [4]. It contains 4322 real-world hazy images, mostly in traffic scenes. There are five categories, including people, bicycles, motorcycles, buses, and cars. A total of 41,203 bounding boxes are labeled [12]. Public transportation systems are monitored and improved with the help of the RTTS dataset. This dataset typically includes location, speed, direction, and stop information for buses, trains, and other public vehicles. It contains various types of data such as GPS data, sensor data, and passenger counts. The RTTS dataset aims to use real-time transport data to make more accurate forecasts, optimize traffic flow, and improve passenger information systems. The RTTS dataset used for training in this study is the dataset produced for the YOLO algorithms on the Roboflow website [33]. The DAWN and RTTS datasets with similar labeling types are used. Figure 3 shows the labeling rates of the objects In the RTTS dataset. Figure 4 shows some images of the RTTS dataset. Additionally, the RTTS dataset containing normal weather conditions is also used for comparison with the DAWN dataset containing AWCs to determine the effects of proposed approach.  
Figure 3: Sample numbers of labeled objects in the RTTS dataset.  
Figure 2: DAWN dataset sample images.