Weapon Detection in Surveillance Videos Using Deep Neural Networks
Muhammad Ekmal Eiman Quyyum, Mohd Haris Lye Abdullah · 2022
Object detection uses computer vision technique to identify and locate objects in an image or video.This feature can help to improve the security level as it can be deployed to detect a dangerous weapon with object detection methods.Driven by the success of deep learning methods, this study aims to develop and evaluate the use the deep neural network for weapon detection in surveillance videos.The YOLOv3 with Darknet-53 as feature extractor is used for detecting two types of weapons namely pistol and knife.The YOLOv3 Darknet-53 is further improved by optimizing the network backbone.This is achieved by adding a fourth prediction layer and customizing the anchor boxes in order to detect the smaller objects.The proposed model is evaluated with the Sohas weapon detection dataset.The performance of the model is evaluated in terms of precision, recall, mean average precision (mAP) and detection speed in frame per second (FPS).