AIRCRAFT TRACKING ON BORDER DEFENCE USING DEEP LEARNING IN VIDEO SURVEILLANCE

Ensuring border security and maintaining national security need the creation of strong defensive systems. Conventional border protection tactics frequently depend on manual surveillance and human intervention, which can be resource-intensive and prone to human mistake. This research suggests a novel method for classifying border protection mechanisms that makes use of deep learning techniques. The primary objective of this project is to develop and implement an intelligent system that can automatically identify and recognize various border defense equipment, such as walls, fences, trenches, and sensor-based systems, using photos and sensor data. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs), in particular, are two types of deep learning algorithms that will be utilized to accomplish this. There will be several steps in the suggested method. At first, a sizable dataset made up of Pictures and sensor data from various border defence systems will be gathered and organised. The deep learning models will be trained on this dataset. While RNNs will be used to extract temporal patterns from sensor data, CNNs will be used to extract spatial characteristics from images. In order to achieve optimal performance, the model parameters will be fine-tuned during the training phase using optimisation approaches.