Work

Detection of Bullet Holes for Target Board in Malaysia Military (ATM) Shooting Exam Application

Year: 2025

Type: conference-paper

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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.os14-3

Authors: Jilian. H. Wai Yin, Idayu M. Tahir, Ammar A Al-Talib, Osama Mohamed Magzoub

Institution: UCSI University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4409913422

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