## 1 Introduction
[25] focused on the field calibration of low-cost particulate matter sensors using artificial neural networks to improve accuracy. [PERSON] et al. [26] discussed traffic level monitoring in urban scenarios through virtual sensing techniques, emphasizing the use of embedded machine learning for real-time traffic monitoring. [PERSON] et al. [27] proposed a fog-enabled air quality monitoring and prediction system, utilizing an optimized lightweight deeplearning model for smart environmental gateways. Lastly, [PERSON] et al. [28] investigated the inference of vehicular traffic in smart cities by integrating IoT devices with machine learning algorithms to improve traffic management and reduce congestion. Existing datasets in the domain of vehicle detection often suffer from incomplete annotations, limited diversity in vehicle types and environmental conditions, insufficient data volume, inconsistent annotation quality, and accessibility issues due to high costs. Many datasets do not label all visible objects, especially those that are truncated, obscured, or extremely small. This results in models that are less effective in real-world scenarios where such conditions are common [29]. Some datasets lack diversity in terms of vehicle types, environmental conditions, and viewpoints. This can lead to models that perform well in controlled environments but fail in more varied, real-world settings [30]. The size of many existing datasets is often insufficient to train deep learning models effectively. This can result in overfitting and poor generalization to new data [31]. The quality of annotations in some datasets is inconsistent, with errors or omissions that can negatively impact model performance [32]. Many high-quality datasets are not publicly accessible or are prohibitively expensive, limiting their use in academic and smaller-scale research projects [33]. The ISTraffic dataset has been meticulously designed to overcome these limitations and provide a more robust foundation for vehicle detection using YOLOv7. Every visible object in the dataset is labeled, regardless of its size, truncation, or occlusion. This ensures that models trained on ISTraffic can handle a wide range of real-world scenarios effectively. The dataset includes a diverse array of vehicle types, environmental conditions, and viewpoints, enhancing the model's ability to generalize across different contexts. Compared to other datasets, the ISTraffic dataset is notable for its exceptionally high number of annotated examples per image. With 36,841 annotated instances and an average of 32.7 annotations per image, ISTraffic provides a substantial amount of data to train deep learning models, reducing the risk of overfitting and improving generalization. The annotations in ISTraffic are carefully reviewed and validated to ensure consistency and accuracy, leading to better model performance. By making ISTraffic publicly available, we aim to support the broader research community and facilitate advancements in vehicle detection technology. By addressing these key limitations, ISTraffic represents a significant improvement over existing datasets, providing a more reliable and comprehensive resource for developing advanced vehicle detection models. The efficacy of training and validating vehicle detection models is profoundly influenced by the availability of well-annotated datasets. While widely used datasets like KITTI [34], COCO [35], BDD100K [36], and PASCAL VOC [37] can serve general object detection tasks, specialized datasets for traffic analysis remain limited. We addressed this gap by introducing the \"ISTraffic\" dataset, an open-source collection that comprised 36,841 annotated instances across 1125 captured images. The dataset covers four vehicle classes: car, light duty vehicles (from now on referred to as \"shuttle\"), bus, and motorcycle. Leveraging the YOLOv7-SORT algorithm, we achieved real-time vehicle detection, counting, and tracking.