AI-Powered Drone Detection Using Convolutional Neural Networks for Enhanced Border Surveillance

Abstract

The advancement of unmanned aerial vehicles (UAVs) or drones has brought about emerging security concerns related to the protection of the border and national security, necessitating the development of effective and efficient drone detection systems. Although drones have immense benefits in various applications, their misuse creates severe threats like smuggling, surveillance, and even hostile acts across borders. Conventional border protection systems, whether radar-based or manual, are less effective in detecting small drones flying at low altitudes due to their low radar cross-sections and irregular flight paths. This paper presents a visual drone aided by Artificial Intelligence. A Convolutional Neural Network (CNN) based detection system. The proposed system is to learn by using deep learning algorithms. the separation of drone objects from others non-drone Visual image data based objects. The CNN model is developed to learn hierarchical spatial features from a series of the model consists of several convolution and pooling layers, followed by fully connected layers. The confusion matrix used for binary classification. The performance of the model is evaluated by traditional evaluation metrics like the accuracy, precision, recall and F1 score, confusion matrix analysis was conducted. and training-validation curves. The experimental outcome shows that the proposed system has an overall classification The test set has 94% accuracy. The proposed system is highly reliable in detecting non-drone objects with a recall of 0.99 and has a precision of 0.85 for drone object detection, thus reducing false alarms. The training and validation curves show that the system has a stable learning process with good generalization capabilities and less overfitting. The proposed system shows that deep learning-based visual detection systems can greatly improve border surveillance systems by offering accurate and scalable drone detection. The proposed system can be used for intelligent security systems and shows the efficiency of artificial intelligence in improving modern border surveillance infrastructure.