A Computer Vision Pipeline for Iterative Bullet Hole Tracking in Rifle Zeroing

TL;DR

This paper introduces an automated computer vision system that detects and tracks bullet holes across multiple shots in rifle zeroing, reducing human error and processing time through advanced detection, data augmentation, and image standardization techniques.

Contribution

The paper presents a novel end-to-end pipeline combining YOLOv8 detection, IoU analysis, and a unique data augmentation method for sequential bullet hole tracking.

Findings

97.0% mean average precision in bullet hole detection

88.8% accuracy in assigning bullet holes to correct iteration

Effective standardization of target orientation improves detection accuracy

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.