Deep Learning Models for Crime Intention Detection Using Object Detection

Abstract—The majority of visual based surveillance
applications and security systems heavily rely on object
detection, which serves as a critical module. In the context of
crime scene analysis, images and videos play an essential role in
capturing visual documentation of a particular scene. By
detecting objects associated with a specific crime, police officers
are able to reconstruct a scene for subsequent analysis.
Nevertheless, the task of identifying objects of interest can be
highly arduous for law enforcement agencies, mainly because of
the massive amount of data that must be processed. Hence, the
main objective of this paper is to propose a DL-based model for
detecting tracked objects such as handheld firearms and
informing the authority about the threat before the incident
happens. We have applied VGG-19, ResNet, and GoogleNet as
our deep learning models. The experiment result shows that
ResNet50 has achieved the highest average accuracy of 0.92%
compared to VGG19 and GoogleNet, which have achieved 0.91%and 0.89%, respectively. Also, YOLOv6 has achieved the highest MAP and inference speed compared to the faster R-CNN.