Erdiyan Ariyanda, Moechammad Sarosa, Sapto Wibowo, Wahyu Nur Hidayat, Yunia Mulyani Azis, Isa Mahfudi
Automatic scoring on shooting targets is traditionally performed manually, which is time-consuming, subjective, and prone to human error. To address these limitations, this study presents a comprehensive performance evaluation of YOLOv8 for automatic bullet impact detection and scoring on paper-based shooting targets. The proposed system formulates bullet hole detection as a single-class small object detection problem and integrates centroid-based localization with a distance-based scoring algorithm. A dataset consisting of 1000 real shooting target images captured using a low-cost ESP32-CAM at 800 × 600 resolution was prepared and manually annotated. The YOLOv8 model was trained and evaluated using standard object detection metrics, including precision, recall, F1-score, mAP@0.5, and m A P@0.5: 0.95. Experimental results show that YOLOv8 achieves precision of approximately 0.75 and recall of approximately 0.52, with mAP@0.5 around 0.565 and mAP@0.5: 0.95 around 0.24. Confidence threshold analysis indicates an optimal threshold near 0.204, yielding the best F1-score of about 0.61. Localization and scoring evaluations demonstrate that the proposed approach can generate scores consistent with ground truth in most cases, achieving an exact score match of 82.1% and 94.6% accuracy within ± 1 score tolerance. The remaining errors are mainly caused by very small, overlapping, and low-contrast bullet holes, particularly near ring boundaries. © 2026 IEEE.
State Polytechnic of Malang, Department of Electrical Engineering, Malang, Indonesia; STIE Ekuitas Bandung, Bandung, Indonesia