Use of Deep Learning for Firearms Detection in Images
Guilherme Vinicius Simões Cardoso, Patrick Marques Ciarelli, Raquel Frizera Vassallo · 2019
Demand for weapons has grown along with crime rates, a contemporary problem haunting countries. This has motivated scientists to devise solutions that can aid in public safety in general. This paper proposes the detection of firearms in images through convolutional neural networks, using the YOLO (You Only Look Once) object detector. To improve learning, YOLO was used to generate annotations in an unmarked database, integrating a new database. This proposal was evaluated in a database containing 608 images, in which 304 images had weapons. Experiments carried out indicated an accuracy of 89.15% and a sensitivity of 100.00%, surpassing results presented in the current literature. These results show that the proposed methodology can be applied for the detection of firearms in images.