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Dangerous Objects Detection Using Deep Learning and First Responder Drone
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Author(s): Zeyad AlJundi (Naif Arab University for Security Sciences, Saudi Arabia), Saad Alsubaie (Naif Arab University for Security Sciences, Saudi Arabia), Muhammad H. Faheem (Naif Arab University for Security Sciences, Saudi Arabia), Raha Mosleh Almarashi (Naif Arab University for Security Sciences, Saudi Arabia), Emad-ul-Haq Qazi (Naif Arab University for Security Sciences, Saudi Arabia)and Jong Hyuk Kim (Naif Arab University for Security Science, Saudi Arabia)
Copyright: 2024
Volume: 16
Issue: 1
Pages: 18
Source title:
International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.367034
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Abstract
Detecting dangerous objects, such as firearms or knives, is crucial for public safety or accurate situational assessment in crime scenes in law enforcement applications. Drones as first responders have been actively utilized for this purpose, showing significant benefits in law enforcement with fast and early detection of such objects. However, automated detection is still challenging, particularly with low-quality drone cameras that operate in low illumination conditions. We evaluate the performance of four popular AI deep learning models to automate the detection of dangerous objects recorded from low-quality drone cameras. The results show that the YOLOv5s model achieves the best detection performance, yielding mAP50 results of 0.964 for color and 0.949 for infrared videos, which are excellent performances considering the low-quality and low-resolution dataset. The trained network model is further implemented as an online web application where law enforcement officers can upload videos taken from drones or CCTV.
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