| Title: | OS14-3 Detection of Bullet Holes for Target Board in Malaysia Military (ATM) Shooting Exam Application |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 423-427 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS14-3 |
| Author(s): | Jilian. H. Wai Yin, Idayu M. Tahir, Ammar A Al-Talib, Osama Mohamed Magzoub |
| Publication Date: | February 13, 2025 |
| Keywords: | Bullet Hole Detection, Military Shooting Exams, Internet of Things (IoT), Machine Learning, YOLOv8 |
| Abstract: | This study focuses on designing and developing a bullet hole detection system for target boards in the Malaysia Army (ATM) shooting exercise environment. The deep learning algorithm is based on YOLO models, utilizing Raspberry Pi and IoT via Blynk for remote monitoring. The prototype includes a Raspberry Pi 4b, HQ Camera Module Lens, 35mm Telephoto Lens, and tripod stand, all at an affordable cost. The study demonstrates that the bullet hole detection system is accurate and effective for ATM shooting exams, meeting SDG 3, SDG 9, SDG 11, and SDG 12 goals. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS14/OS14-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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