## 1 Introduction  
Urban areas face mounting challenges related to traffic congestion, safety, and environmental impact. The proliferation of vehicles on city roads exacerbates these issues, necessitating innovative solutions for effective traffic management [1]. Real-time vehicle detection systems offer a promising avenue to address these challenges by providing timely and accurate information about vehicle counts and movements. Recent studies have aimed to estimate vehicle-borne emissions [2; 3; 4]. Over the years, numerous object detection algorithms have been developed, including two-stage detection algorithms such as Region-based Convolutional Neural Networks (RCNNs), Fast-RCNN [5], Faster-RCNN [6], Region-based fully convolutional network (R-FCN) [7], and single-stage detection algorithms, including Single Shot Detector (SSD) [8], RetinaNet, and You Only Look Once (YOLO) [9; 10]. Although each algorithm has its own strengths and weaknesses, two-stage concepts often experience slower inference speeds compared to detection methods based on single-stage concepts, such as YOLO. YOLO is a popular open-source algorithm that is used for real-time object detection and classification with its high speed and accuracy. In recent years, advancements in object detection algorithms have significantly enhanced the capabilities of various applications [11; 12]. Notably, [PERSON] et al. [11] presented an improved YOLOv5 for detecting mineral zoning in complex conditions, showcasing advancements in feature detection. Additionally, the RailFOD23 research combines GPT and YOLO for foreign object detection on railroad lines, demonstrating enhanced detection capabilities through this integration [12]. The first version of the algorithm was developed by [PERSON] et al. (2016) [9], and several versions have been released in recent years. The algorithm achieves end-to-end object detection using a single Convolution Neural Network (CNN) and predicts bounding boxes and class labels in a single pass. This approach enables rapid processing, making it suitable for real-time applications. For instance, when processing camera frames, YOLO operates at significantly higher speeds, leading to accurate real-time vehicle counts. However, real-world traffic scenarios introduce complexities. Factors like varying traffic density and lighting conditions can hinder accurate detection and re-identification. These challenges impact the reliability of vehicle counting. Combining tracking algorithms with YOLO helps to address these challenges. Therefore, many researchers obtain more accurate traffic features and achieve stable tracking of vehicles using Simple Online and Real-time Tracking (SORT) [13; 14] and DeepSORT [15; 16; 17; 18] benchmarks. In the realm of computer vision and transportation research, numerous studies have explored vehicle detection and counting using camera images and video streams [19; 20; 21; 22]. [PERSON] and [PERSON] [19] leveraged the YOLOv7 framework for vehicle identification using CCTV data, achieving an impressive detection accuracy exceeding 90%. [PERSON] et al. [24] pre-trained YOLOv3 for object detection across five vehicle classes, reporting an overall accuracy of 85.45% on UA-DETRAC dataset test videos. [PERSON] and [PERSON] [23] reported that the YOLOv3 algorithm, evaluated using the COCO dataset, achieved an accuracy of 44% during daylight hours and 41% under night-time conditions for CCTV cameras. [PERSON] and [PERSON] [13] trained YOLO-SORT for six classes and achieved an overall accuracy of 0.72 across diverse video sequences. [PERSON] et al. [14] demonstrated the efficacy of the YOLO-SORT algorithm, achieving a remarkable accuracy score of 0.95 specifically for car detection. Notably, they emphasized the need to consider night-time scenarios, accounting for potential lighting variations throughout the year. Recent studies have also explored alternative methods for traffic monitoring and environmental assessment. [PERSON] et al.