Refining Transportation Automation with Convolutional Neural Network-Based Vehicle Detection via UAVs

In response to the surge in private vehicle usage exacerbated by the pandemic, transportation infrastructure faces heightened demands for efficiency and safety. This trend, fueled by concerns over social distancing and changes in work patterns, has intensified traffic congestion in urban centers and tourist destinations. Consequently, local governments are exploring strategies such as smart traffic management systems and alternative transportation modes to alleviate congestion and promote sustainability. However, the escalating vehicular influx has led to a surge in traffic accidents and air pollution, posing significant threats to public health and ecological balance. Continual monitoring of road traffic is crucial for preempting traffic bottlenecks, averting accidents, and promoting sustainable transportation practices. Advanced technologies, including unmanned aerial vehicles (UAVs), have emerged as powerful tools for real-time traffic monitoring. Equipped with high-resolution cameras and sensor systems, UAVs facilitate efficient data acquisition from desired regions, offering advantages over traditional aerial vehicles. While UAVs present promising solutions, real-time utilization poses challenges such as complex background scenes and detection misses. Additionally, advancements in object detection algorithms, particularly the You Only Look Once (YOLO) algorithm, have revolutionized vehicle detection. This study focuses on evaluating vehicle detection using YOLOv9, the latest iteration, and compares it with YOLOv7, its predecessor, leveraging aerial monitoring via UAVs. By elucidating the innovations introduced by YOLOv9, this study contributes to enhancing traffic monitoring capabilities and promoting effective transportation management strategies.