Real-time Transformer Vandalism Detection by Application of Tuned Hyper Parameter Deep Learning Model

Abstract

Vandalism is an illegal act of cannibalism or change of face to a private or public property by human beings for re-sale of parts or to punish the property owner. Initial research findings on transformer Vandalism detection have fallen short of human image recognition of the vandal in real-time but only does detection of activities after the damage is done or as it occurs. Automated real-time systems using sensor feed to a trained deep learning model is a new transformer vandalism detection approach with capabilities of three-dimensional image learning, extracting important image features automatically and temporal output prediction. This paper aims at distinguishing the human object entering a zoned transformer area without permission to take away or modify the established infrastructure, so that the Vandal can be arrested before causing any damage to the transformer. The researchers identified a multiplicative hybrid model combining convolutional neural networks and long short-term memory for application to vandalism problem to detect the image of a vandal as it enters a restricted transformer installation site. The image recognition accuracy can be improved by tuning the model hyper-parameters and the specific hyper-parameters considered in this research work are number of model layers and epochs. The human object is distinguishing by applying the image features taken with Image sensor to a trained deep learning model. The hybrid deep learning method increases the output prediction accuracy from the input data and lowers computational processing complications due to a reduced data volume through pooling. The system is trained and validated using ImageNet dataset. Results achieved by five layers and sixty epochs is 99% recognition accuracy. The performance of the system with an increased number of layers and epochs to five and sixty respectively was the best result as compared with lower layers and epochs. Further increase of these parameters resulted to system overfitting.