## 1 Introduction
Among them, [4; 7; 8; 9; 12; 14; 15] mainly involve object detection using DL for AWCs. Reference [4] is based on the Realistic Single-Image Dehazing (RESIDE) dataset consisting of real and computer-generated hazy images. The aim of the study was to measure the limits of algorithms to remove haze in this RESIDE dataset and to compare them with each other using YOLOv3. But, it was used for the VOC_Foggy dataset containing only foggy weather conditions. Additionally, the used datasets do not contain trucks. In [7], Anchor-Free YOLOv4 was implemented to speed up vehicle detection. Using this model enabled a reduction in the required number of predictions per location from three to one. As a dataset, they used BDD-IW derived from the BDD100K dataset by adding fog to the images. Although it contains only sunny, rainy, and snowy weather conditions, there are no sandstorms conditions. Also, the old version YOLOv3 was used. The work in [8] proposes the Image-Adaptive YOLO (IA-YOLO) model to reduce the difficulty of object detection in AWCs. A differentiable image processing module is used to detect AWCs. But, it uses the VOC_Foggy dataset containing only foggy weather conditions. In [9], the authors compare the performance of DL algorithms for detecting road surface conditions of autonomous vehicles at night and in AWCs. For this purpose, the VGG, CNN, DenseNet, and SqueezeNet models are preferred for this comparison process. However, it does not contain sandstorm conditions, and it focuses on the road surface conditions at night in view of dry, wet, and snowy weather. There is no detection of objects such as buses, trucks, and persons. Moreover, it uses the older YOLOv2. In [12], it is emphasized that the performances of object detection algorithms decrease in AWCs. In order to improve the performance, it is emphasized that the image should be improved first. The proposed method combines the hazing module and the detection module and proposes an architecture called BAD-Net. But, it contains only foggy weather conditions. Additionally, the used datasets do not contain trucks. In [19], a technique of combining multiple deep learning models and data augmentation is proposedto overcome the difficulty of object detection in AWCs. RetinaResnet50, YOLOv3, and SSD are used for the DAWN dataset. However, the performance results are lower according to this paper. As a result, the studies in the literature are either not comprehensive or appear to have low success in terms of AWCs. Recently, metaheuristic algorithms have shown their effectiveness in applications in different fields, including path planning [20], image segmentation [21], feature selection problems [22], software engineering [23], artificial intelligence and machine learning [24], control engineering [25], structural engineering [26], and biological applications [27]. The Grey Wolf Optimizer (GWO) [28], Artificial Rabbit Optimizer (ARO) [29], and Chimpanzee Leader Selection Optimization (CLEO) [30], which are inspired by the hunting methods of wolves and the foraging methods of rabbits, are prominent metaheuristic algorithms for finding optimal solutions in complex search areas [25; 31]. GWO is based on the hunting strategy and hierarchical structure led by alpha, beta, and delta grey wolves. ARO was developed by observing the foraging behavior of rabbits in nature. The CLEO algorithm is an optimization algorithm inspired by the social hierarchy and decision-making processes of chimpanzee communities, particularly the role of the alpha male chimpanzee as a leader. The aforementioned studies in the literature generally focus on the changing structure of DL algorithms to improve object detection performance based on DL. In addition to them, there are a few studies on the optimization of the structure, such as [15], where a boosted region proposal network (BRPN) is developed to overcome the shortcomings of Faster R-CNN. However, the datasets not containing AWCs are used to test BRPN. The loss function is improved by the metaheuristic algorithm GWO in the study.