应用 YOLOv8 和 Detectron2 检测射击卡上的弹孔并计算得分

AI Pub Date : 2023-12-22 DOI:10.3390/ai5010005
Marya Butt, Nick Glas, Jaimy Monsuur, Ruben Stoop, Ander de Keijzer
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引用次数: 0

摘要

射击运动中的目标计分是一项关键且耗时的任务,需要依靠人工计算弹孔。本文介绍了一种使用物体检测技术的自动得分检测模型。这项研究通过比较七个模型(属于两种不同的架构设置)的性能以及公开数据集,为计算机视觉领域做出了贡献。另一个增值之处是,研究中包含了最近于 2023 年(撰写本文时)发布的物体检测模型 YOLOv8 的三个变体。所使用的模型中有五个是单发探测器,两个属于双发探测器。数据集是从射击场手动获取的,并通过使用 Python 代码生成更多通用数据进行扩展。在训练数据集以开发模型之前,使用 Roboflow API 调整了数据集的大小(640 × 640)并对其进行了扩充。然后在测试数据集上对训练好的模型进行评估,并使用 mAP50、mAP50-90、精确度和召回率等矩阵对它们的性能进行比较。结果表明,YOLOv8 模型能以良好的置信度检测到多个对象。在这些模型中,YOLOv8m 的表现最好,mAP50 值最高,达到 96.7%,其次是 YOLOv8s,mAP50 值为 96.5%。我们建议,如果系统要在实时环境中实施,YOLOv8s 是一个更好的选择,因为它的推理时间(2.3 毫秒)比 YOLOv8m(5.7 毫秒)少得多,而 mAP50 值却高达 96.5%。
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Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards
Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduces an automatic score detection model using object detection techniques. The study contributes to the field of computer vision by comparing the performance of seven models (belonging to two different architectural setups) and by making the dataset publicly available. Another value-added aspect is the inclusion of three variants of the object detection model, YOLOv8, recently released in 2023 (at the time of writing). Five of the used models are single-shot detectors, while two belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640 × 640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among these models, YOLOv8m performed the best, with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3 ms) than YOLOv8m (5.7 ms) and yet generated a competitive mAP50 of 96.5%.
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