Fire and Gun Violence based Anomaly Detection System Using Deep Neural Networks

Real-time object detection to improve surveillance methods is one of the sought-after applications of Convolutional Neural Networks (CNNs). This research work has approached the detection of fire and handguns in areas monitored by cameras. Home fires, industrial explosions, and wildfires are a huge problem that cause adverse effects on the environment. Gun violence and mass shootings are also on the rise in certain parts of the world. Such incidents are time-sensitive and can cause a huge loss to life and property. Hence, the proposed work has built a deep learning model based on the YOLOv3 algorithm that processes a video frame-by-frame to detect such anomalies in real-time and generate an alert for the concerned authorities. The final model has a validation loss of 0.2864, with a detection rate of 45 frames per second and has been benchmarked on datasets like IMFDB, UGR, and FireNet with accuracies of 89.3%, 82.6% and 86.5% respectively. Experimental result satisfies the goal of the proposed model and also shows a fast detection rate that can be deployed indoor as well as outdoors.