arXiv:2601.17062v1 Announce Type: cross
Abstract: Adjusting rifle sights, a process commonly called “zeroing,” requires shooters to identify and differentiate bullet holes from multiple firing iterations. Traditionally, this process demands physical inspection, introducing delays due to range safety protocols and increasing the risk of human error. We present an end-to-end computer vision system for automated bullet hole detection and iteration-based tracking directly from images taken at the firing line. Our approach combines YOLOv8 for accurate small-object detection with Intersection over Union (IoU) analysis to differentiate bullet holes across sequential images. To address the scarcity of labeled sequential data, we propose a novel data augmentation technique that removes rather than adds objects to simulate realistic firing sequences. Additionally, we introduce a preprocessing pipeline that standardizes target orientation using ORB-based perspective correction, improving model accuracy. Our system achieves 97.0% mean average precision on bullet hole detection and 88.8% accuracy in assigning bullet holes to the correct firing iteration. While designed for rifle zeroing, this framework offers broader applicability in domains requiring the temporal differentiation of visually similar objects.
