用视觉算法自动追踪枪械瞄准时的弹孔，提升校准效率与精度。
A Computer Vision Pipeline for Iterative Bullet Hole Tracking in Rifle Zeroing
- 结合YOLOv8与交并比分析，实现弹孔精准检测与迭代追踪。
- 弹孔检测平均精度达97.0%，迭代归属准确率达88.8%。
- 适合军事训练、射击竞赛及需追踪相似目标的场景。
调整枪械瞄准镜（称为“零位校准”）需要识别并区分多次射击产生的弹孔。传统方法依赖人工观察，受靶场安全规程限制，耗时且易出错。本文提出一个端到端的计算机视觉系统，直接从射击线拍摄的图像中自动检测弹孔并进行基于迭代的追踪。方法结合YOLOv8实现小目标精确检测，并利用交并比（IoU）分析区分连续图像中的弹孔。针对标注序列数据稀缺问题，提出一种新颖的数据增强策略，通过移除物体而非添加，模拟真实的射击序列。此外，引入基于ORB的透视校正预处理流程，统一靶标朝向，提升模型性能。系统在弹孔检测上达到97.0%的平均精度，在弹孔归属正确迭代方面达到88.8%的准确率。尽管专为枪械零位校准设计，该框架也可推广至需区分时间序列中视觉相似对象的其他领域。
原文摘要 · Abstract (English)
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.
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