Unauthorized Drone Detection Using Deep Learning

Abstract: The growing concerns over drone misuse in national airspace, including activities like drug smuggling and privacy violations, demand advanced drone detection systems. Traditional methods struggle to accurately identify drones among various airborne objects. YOLO v8, renowned for its efficient single-pass object detection, presents a sophisticated solution. Through training on annotated datasets featuring drones in diverse environments and fine-tuning with aerial imagery, YOLO v8 excels at recognizing drones amidst complex backgrounds. Its multi-scale detection capabilities enable it to detect drones of varying sizes, crucial for different distances from the camera. Real-time prediction ensures timely detection, vital for immediate responses to unauthorized intrusions. Integration with drone tracking systems enhances situational awareness, facilitating proactive measures. Furthermore, employing adaptive thresholding and post-processing techniques like Kalman filtering refines drone detection accuracy, reducing false positives. YOLO v8 thus emerges as a potent tool in mitigating risks associated with drone misuse, bolstering defenses against unauthorized activities in national airspace.