## 2 Dataset Description and Applied Algorithms
### Applied Algorithms  
In this study, the DL algorithms YOLOv5, YOLOv7, and YOLOv9 were preferred for object detection. There are many studies on YOLOv5. YOLOv5 was chosen to compare the performance of the other two more recent algorithms in AWCs. Optimization algorithms were used to improve the hyperparameters of the YOLO models, especially the performance of the algorithms in AWCs. Various optimization algorithms can be used for this task. In this study, we chose GWO for object detection and the more recent successful ARO and CLEO metaheuristic algorithms for comparison. **YOLO version 5 (YOLOv5)**: YOLOv5 is part of the YOLO family for object detection, optimizing previous versions. YOLOv5 was developed by the Ultralytics company. Although there is no article in any peer-reviewed journal, it is preferred in scientific studies and shows effective results [5]. The steps related to the image to be detected using YOLOv5 are shown in Figure 5. The image arriving at the input layer is sent to the backbone for processing and feature extraction. The spine obtains feature maps with different dimensions and combines them with the neck to produce feature maps P3, P4, and P5. Finally, these maps are combined with the head to produce Bounding Boxes (Bboxes) that enclose the image in a rectangular frame [13]. **YOLO version 7 (YOLOv7)**: The YOLOv7 [34] algorithm has been widely recognized for its superior speed and accuracy in object detection, outperforming other established object detection models within the scope of 5 FPS to 160 FPS [35]. In the realm of urban infrastructure and transportation, YOLOv7 has been employed for road feature detection, pothole detection, and pedestrian-vehicle detection, demonstrating its significance in enhancing road safety and infrastructure maintenance [36; 37]. YOLOv7 is one of the most recent official versions of YOLO released by the authors of the YOLO architecture in 2022. The architecture of YOLOv7 depends on a standard framework for object detection, which comprises three key elements: the backbone, neck, and head (see Figure 6). The backbone  
Figure 4: RTTS dataset sample images. Figure 5: YOLOv5 default inference flow chart [13]. functions as the feature extractor and is tasked with capturing hierarchical features from the input images. The backbone of the YOLOv7 architecture typically leverages a CNN (convolutional neural network) structure, such as CSPDarknet or EfficientNet, to efficiently extract features across various scales. The neck module, which follows the backbone, provides a collection of neural network layers that combine and mix features to the head section, which outputs the prediction. The head section takes the mixture of features from the neck and outputs a prediction. **YOLO version 9 (YOLOv9):** YOLOv9 [38] is the latest YOLO algorithm, released in February 2024. When creating YOLOv9, two architectures were combined to improve its performance compared to previous models. Programmable Gradient Information (PGI) and a Generalized Efficient Layer Aggregation Network (GELAN) are combined to create YOLOv9. PGI, which prevents the loss of critical information in the trained data as it passes through multiple layers, makes learning more efficient. GELAN improves the performance of the model by performing optimization operations during the collection and processing of information in the layers. By combining these two architectures, YOLOv9 becomes a robust choice for object detection applications. The PGI and the related network architecture is given in Figure 7. The PGI consists of three main components: (1) a main branch that makes inferences, (2) an auxiliary reversible branch that provides reliable gradients for inference, and (3) multilevel auxiliary information. Multilevel semantic information can be learned to control main branch learning. As seen in Figure 7d, PGI uses only the main branch in its inference process, thus resulting in no additional cost. In deep learning methods, the other two components assist in solving or slowing down several important problems.