INTELLIGENT SYSTEM FOR AUTOMATIC DETECTION AND SCORING OF SHOOTING TARGETS BASED ON COMPUTER VISION AND MICROCONTROLLER TECHNOLOGIES

Abstract

This paper presents an intelligent system for the automatic detection and scoring of shooting targets based on the Raspberry Pi 3 microcontroller platform and computer vision technologies. The objective of the study is to develop an autonomous and highly accurate yet low-cost complex capable of recording and analyzing shooting results without human intervention.

The system integrates mechatronic and algorithmic components, including Nema 17 stepper motors, color sensors, a webcam, and a server-side image processing module, forming a unified cyber-physical architecture. The algorithmic core is based on geometric calibration using homography, adaptive illumination equalization via CLAHE, and a radial precision evaluation model. To detect bullet holes, a modified YOLOv8-Nano neural network architecture was employed, optimized for recognizing low-contrast circular targets.

Experimental results confirmed the high accuracy and robustness of the proposed approach: under stable lighting conditions, the system achieved a spatial recognition precision of ±2 mm with a response time below 0.2 seconds. The training and validation curves of the model demonstrate smooth convergence and stable generalization, confirming the correctness of the architectural modifications and the optimization of the loss function.

The scientific novelty of this work lies in the integration of a mechatronic framework and deep-learning algorithms into a unified real-time system that enables automatic target replacement, image processing, and result visualization through a web interface. The practical significance is in the potential application of the system in sports schools, mechatronics laboratories, training centers, and research test ranges requiring accurate and autonomous shooting evaluation.

Future work will focus on extending system capabilities through the integration of advanced neural network algorithms (YOLOv8, Detectron2), cloud-based technologies, and automatic camera stabilization, further improving accuracy and autonomy while maintaining low implementation cost.