PEMETAAN STATISTIK SKOR DAN AKURASI LATIHAN MENEMBAK BERBASIS OPENCV DAN VISUAL STUDIO PADA ANTARMUKA OTOMATIS
Keywords:
Scoring system, Air rifle pistol, Visual Studio, Python, OpenCV, Image analysis, Automated assessmentAbstract
This research develops an interface model for an automated shooting practice assessment system integrated with Visual Studio, Python, and OpenCV to provide real-time shot adjustment feedback. The system is designed to detect shots on air rifle pistol and long-barreled firearm targets, focusing on shot adjustment guidance based on shot coordinates, as shown in Figure 4.9. Utilizing OpenCV-based shot adjustment algorithms, the system achieves 100% accuracy in detecting random shots and provides guidance such as “Shot too far left, aim right” or “Good Shot”. With an average response time of 0.5 seconds per shot, the system enhances the efficiency of TNI shooting training through an adaptive and user-friendly interface.
This research discusses the design of an interface model for an automated shooting practice assessment system that integrates Visual Studio, Python, and OpenCV technologies. The system is designed to provide an automated solution for scoring shots, efficiently detecting and calculating the results of shots on air rifle pistol targets as well as long-barreled firearms. By using Visual Studio and Python as the primary programming languages and OpenCV as the image processing library, this system has the potential to enhance the effectiveness and objectivity of evaluating the performance of shooting practice participants through a user-friendly interface visualization.
The interface model of the automated shooting practice assessment system, integrated with an OpenCV-based sensor system, successfully detected and evaluated shots using a random technique with an average success rate of 100%. This is evidenced by the compatibility between the interface design and the point-based correction decision algorithm and the shot counting algorithm. The system is capable of processing shooter data from input to generating real-time PDF reports with an average response time of 0.5 seconds per shot, providing comprehensive performance analysis. This is proven by the consistency of the output between the interface design and the PDF report printing algorithm.
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