Anti-Drone System Using Machine Learning

Unmanned aerial vehicles (UAVs), commonly known as drones, have gained popularity for various applications. However, their misuse and potential security threats have prompted the development of anti-drone systems. This paper proposes an anti-drone system that utilizes cameras and the R-CNN (Region-based Convolutional Neural Network) framework for drone detection and classification. The system leverages a network of cameras strategically positioned in the surveillance area. Each camera captures real time video feed, which is processed by the R-CNN model to identify potential drone instances. The R-CNN framework is trained on a large dataset of annotated drone images, enabling it to learn discriminative features and accurately recognize drones. The proposed system employs a multi-stage approach to detect and classify drones. Initially, object proposals are generated by extracting regions of interest from the video frames. These proposals are then passed through the R-CNN model, which extracts features and predicts the presence of drones. Furthermore, the system incorporates a tracking mechanism to maintain the continuity of detection across multiple frames, reducing false alarms and improving accuracy. To evaluate the system's performance, extensive experiments were conducted on various real-world scenarios. The results demonstrate the effectiveness of the proposed anti-drone system in detecting and classifying drones with high accuracy and low false-positive rates. The system proves particularly robust against challenging conditions such as varying lighting, background clutter, and occlusions. Overall, this paper presents an innovative approach to counter the growing security concerns posed by drones. By combining cameras and the R-CNN framework, the proposed anti-drone system achieves reliable and efficient detection and classification, offering a valuable tool for safeguarding critical infrastructures, public events, and sensitive areas from potential drone threats.