Smart Border Surveillance: Real-time CNN on Drones with IoT Integration

Border security is a critical priority, but solutions must balance efficacy with ethical considerations. This study examines a proposed system using convolutional neural networks (CNNs) on drone footage to identify border guards and locals by clothing types, aiming to increase security and accountability. The system integrates with ThingSpeak for real-time data collection. While employing advanced technologies like computer vision and IoT could aid border monitoring, safeguards are needed regarding privacy rights, bias prevention, and human oversight. The potential benefits of such a system, including detecting unauthorized crossings and monitoring guard activity levels, require careful weighting against risks like overreach and profiling. Further research should analyze larger social impacts and incorporate checks against errors. With deliberate multi-disciplinary development, AI-enabled border surveillance could provide valuable supplemented information to human agents, though not autonomous enforcement decisions. The suggested system provides an efficient and effective solution for border surveillance and guard accountability by utilizing cutting-edge technologies such as CNNs, computer vision, and IoT integration.