IoT Inflated Defense Surveillance Entity Applying Human Intelligence via Faster R-CNN: An Approach

With a deep learning perspective, we describe a novel approach towards object detection effectively at sensitive border control regimes (the so-called Line of Control) by using a neural network approach viz. region convolutional neural network (R-CNN). This approach facilitates faster, accurate and efficient Smart Intrusion Disclosure Entity (SIDE) using Human Intelligence via Internet of Things (IoT). To achieve this goal, we propose an algorithm that displays image processing and deep learning positioned data to separate intruders as a hazard and alerts the defense control center accordingly. The image captured is discursive towards elements by utilizing Faster R-CNN innovation. The lineaments are eradicated from the training posture by utilizing a unique model. Classification is done by using Faster R-CNN algorithm. The percentage accuracy of an image is analyzed in terms of precision, accuracy, recall, specificity and F1 score. This demonstrates that the intended algorithm is robust, accurate and precise. Our technique accomplishes the exactness focused on the Faster R-CNN innovation which is faster during both training and inference.