  Gun violence is a severe problem in the world, particularly in the United
States. Deep learning methods have been studied to detect guns in surveillance
video cameras or smart IP cameras and to send a real-time alert to security
personals. One problem for the development of gun detection algorithms is the
lack of large public datasets. In this work, we first publish a dataset with
51K annotated gun images for gun detection and other 51K cropped gun chip
images for gun classification we collect from a few different sources. To our
knowledge, this is the largest dataset for the study of gun detection. This
dataset can be downloaded at www.linksprite.com/gun-detection-datasets. We
present a gun detection system using a smart IP camera as an embedded edge
device, and a cloud server as a manager for device, data, alert, and to further
reduce the false positive rate. We study to find solutions for gun detection in
an embedded device, and for gun classification on the edge device and the cloud
server. This edge/cloud framework makes the deployment of gun detection in the
real world possible.
