A Computer Vision Pipeline for Iterative Bullet Hole Tracking in Rifle Zeroing
Robert M. Belcher, Brendan C. Degryse, Leonard R. Kosta, Christopher J. Lowrance

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…
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Taxonomy
TopicsTraumatic Ocular and Foreign Body Injuries · Image and Object Detection Techniques · Advanced Measurement and Detection Methods
