基于深度转移学习的足球无球检测在现场板球比赛中
Sudhakar Das1, Tanjim Mahmud1, Dilshad Islam2
1Rangamati Science and Technology University, Rangamati, Bangladesh.
Computational intelligence and neuroscience
|June 29, 2023
概括
这项研究引入了一个人工智能框架,用于在板球中自动检测无球,达到0.98准确度. 该系统增强了决策,减少了错误,改善了游戏.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运动技术技术 运动技术
背景情况:
- 自动化和人工智能,特别是机器学习,越来越多地用于各种领域的决策.
- 人工智能技术正被整合到板球等体育运动中,以减少人类错误并改善游戏分析.
- 板球的不可预测性使得准确的裁判至关重要,因为错误的决定可以显著影响比赛结果.
研究的目的:
- 开发一种自动化系统,使用人工智能在板球中准确检测无球.
- 通过尽量减少裁判错误来提高板球比赛的公平性和诚信.
- 创建一个智能系统,减少因判断错误而产生的争议,并促进更健康的游戏环境.
主要方法:
- 数据收集和处理,专注于投手的端通过裁剪.
- 图像增强技术,以提高清晰度和减少视觉数据中的噪声.
- 培训和测试优化卷积神经网络 (CNN),包括修改的预训练模型,如VGG16和VGG19.
主要成果:
- 拟议的框架实现了0.98的准确性,用于自动检测无球.
- 两种VGG16和VGG19模型都达到了0.98准确度.
- 由于其优越的召回价值,VGG16被选为拟议的模型.
结论:
- 开发的AI框架为板球中自动无球检测提供了高度准确的解决方案.
- 该系统展示了人工智能在体育运动中的潜力,以改善裁判和减少争端.
- 使用增强的图像处理和优化的CNN模型,如VGG16,对于实时体育分析是有效的.
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