Publication: Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System
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Issued Date
2021-01-01
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2-s2.0-85125180961
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Mahidol University
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SCOPUS
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ICSEC 2021 - 25th International Computer Science and Engineering Conference. (2021), 400-405
Suggested Citation
Narit Hnoohom, Pitchaya Chotivatunyu, Nagorn Maitrichit, Virach Sornlertlamvanich, Sakorn Mekruksavanich, Anuchit Jitpattanakul Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System. ICSEC 2021 - 25th International Computer Science and Engineering Conference. (2021), 400-405. doi:10.1109/ICSEC53205.2021.9684649 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76710
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Title
Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System
Abstract
Thailand has faced unrest in recent years, as have other countries around the world. The continuation of present trends means a tendency for an increase in both crimes against people and property. Nowadays, CCTV technology is widely used as surveillance and monitoring tools to help keep people safe. However, most of them still rely primarily on police personnel to inspect the displays. A weapon detection system can reduce the screen-reading workload of police officers with a limited workforce. The integration of weapon detection with CCTV cameras has a role to play in solving the problem. To develop the weapon detection system, the datasets used in this research were collected from 2 public datasets: ARMAS Weapon detection dataset and IMFDB Weapon detection system. The object detection method was used from TensorFlow Object Detection API using 1) SSD MobileNet-V1, 2) EfficientDet-D0 and 3) Faster R-CNN Inception Resnet-V2. For all experimental results, the object detection model is the Faster R-CNN Inception V2 using Dataset 1, ARMAS Weapon detection dataset, with the highest mAP of 0.540 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.793 and 0.627, respectively.