Comparative Analysis of SSD and Faster R-CNN in UAV-Based Vehicle Detection

Artificial intelligence-based methods for monitoring transportation networks and vehicles play a crucial role in enhancing forensic analysis and security applications. Continuous surveillance enabled by object detection algorithms allows real-time monitoring of roads and highways, facilitating tasks such as traffic flow monitoring, accident detection, and identification of suspicious vehicles or behaviors. Integrating these algorithms into surveillance systems supports law enforcement in swiftly locating vehicles of interest and responding effectively to incidents, thereby improving security measures and enhancing forensic investigations through detailed analysis of surveillance footage. Furthermore, object detection aids in optimizing traffic management by identifying congestion points and optimizing traffic signals, thus enhancing road safety and mobility. This study evaluates the performance of SSD and Faster R-CNN in vehicle detection using UAV-based aerial imaging, providing insights into their strengths and limitations for applications such as aerial surveillance and traffic monitoring. By comparing these algorithms comprehensively, this study aims to guide the selection of the most suitable model for effective vehicle detection in diverse operational environments. The findings contribute to advancing AI applications in transportation and security, offering insights into optimizing surveillance systems for enhanced safety, efficiency, and responsiveness in managing urban mobility and security challenges.