## 1 Introduction  
Object detection studies can generally be divided into two main focus points: improvement of image processing methods and DL algorithms for one-stage or two-stage object detection. Image processing methods such as [1, 2, 3, 4, 5, 6] generally involve performing operations on an image to make it better for object detection. DL algorithms for object detection, such as [7, 8, 9, 10, 11, 12, 13, 14, 15, 16], are involved in improving the performance of DL architectures such as YOLO, Single Shot Detection (SSD), Faster RCNN, RetinaNet, CoupleNet CNN, SqueezeNet, VGG, ResNet, and DenseNet. There are two approaches, one-stage or two-stage approaches, for object detection algorithms, as mentioned above. The one-stage approach means that object detection is performed by processing and analyzing all image pixels at once. The two-stage approach has two stages. In the first stage, region proposals are created, i.e., potential object regions are identified. In the second stage, object detection is performed in these regions. Both approaches have advantages over each other. While the single-pass approach emphasizes speed in object detection, the double-pass approach emphasizes higher accuracy. Since YOLO is faster and more efficient among the single-stage methods, such as SSD as in [17], and the detection speed is more important, especially for autonomous vehicles among the real-time applications, this paper aims to improve the performance of YOLO in adverse weather conditions (AWCs). For these and similar reasons, YOLO is commonly used for object detection in the literature, such as [1; 2; 3; 7; 8; 10; 11; 13; 14; 16]. For example, reference [1] proposes approaches that first perform image enhancements and then object detection. The TogetherNet module of [1] proposes a unified detection module for performing both image restoration and object detection using some versions of YOLO version x (YOLOx). In [2], the multi-scale retinex with color restoration (MSRCR) algorithm is used for pre-processing in vehicle detection in foggy weather, where YOLOv4 is used for object detection. The focus on foggy weather only in [2] does not provide sufficient data to measure the object detection performance of AWCs. In [3], a method for enhancing visibility and a robust approach for vehicle detection and tracking based on deep learning are Introduced. To solve the problem of low accuracy detection in AWCs, a visibility restoration method called Automatic White Balance Fused with Laplace Pyramid (AWBLP) is proposed. The DAWN dataset--containing objects such as cars, people, bicycles, buses, trucks, and motorcycles [18] under all AWCs, including fog, snow, rain, and sandstorms--is used, together with YOLOv3 and AWBLP. The YOLOv3 model used here was released In 2018. Considering the mAP (mean Average Precision) values in the study, the YOLOv3 model is slightly behind in performance. Another paper uses transfer learning for vehicle detection by adjusting the pre-trained architecture of YOLO-v5 through fine-tuning [10]. The DAWN dataset is used as the dataset. In [13], an algorithm was developed to better detect small objects. These algorithms were combined with YOLOv5 to create the 5f-YOLOv5 model. In [14], an improved YOLOv5 model for object detection in only foggy environments was proposed. A feature enhancement module (FEM) structure was created to detect foggy images, and this module enabled YOLOv5 to improve object detection performance. Object detection studies in AWCs are one of the most emphasized topics in recent years. Researchers have focused on applying and improving object detection algorithms in autonomous vehicles with challenging problems [1; 6; 7]. In particular, the success of the vehicles in AWCs is important for performing safe and effective driving. Developments in computer vision and artificial intelligence have led to some successful results in difficult weather conditions for object detection, such as AWCs [1; 2; 3; 4; 6; 7; 8; 9; 12; 14; 19].