학술논문

Gun Detection System Using Yolov3
Document Type
Conference
Source
2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Smart Instrumentation, Measurement and Application (ICSIMA), 2019 IEEE International Conference on. :1-4 Aug, 2019
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Videos
Real-time systems
Benchmark testing
Training
Detectors
Machine learning
Weapons
yOLOV3
handgun detection
False Positive
Language
ISSN
2640-6535
Abstract
Based on current situation around the world, there is major need of automated visual surveillance for security to detect handgun. The objective of this paper is to visually detect the handgun in real time videos. The proposed method is using YOLO-V3 algorithm and comparing the number of false positive and false negative with Faster RCNN algorithm. To improve the result, we have created our own dataset of handguns with all possible angles and merged it with ImageNet dataset. The merged data was trained using YOLO-V3 algorithm. We have used four different videos to validate the results of YOLO-V3 compared to Faster RCNN. The detector performed very well to detect handgun in different scenes with different rotations, scales and shapes. The results showed that YOLO-V3 can be used as an alternative of Faster RCNN. It provides much faster speed, nearly identical accuracy and can be used in a real time environment.