DeepGuard:使用Golden Jackal优化与深度学习模型进行实时威胁识别
Fatma S Alrayes1, Hamed Alqahtani2, Wahida Mansouri3,4
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|April 16, 2025
概括
本研究介绍了DeepGuard模型,用于使用深度学习和金子优化在监控视频中实时检测暴力. 该模型在识别威胁方面实现了高精度,提高了公共安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 安全系统安全系统
背景情况:
- 在监控中承认暴力对于公共安全和关键基础设施安全至关重要.
- 深度学习 (DL) 为分析视频录像中复杂的视觉模式提供了先进的功能.
- 传统的安全措施可以通过智能,实时威胁检测系统来增强.
研究的目的:
- 开发和评估DeepGuard模型,用于监控视频中实时识别暴力和非暴力事件.
- 利用深度学习和优化技术来提高威胁检测准确度.
- 加强公共空间和关键基础设施的积极安全措施.
主要方法:
- 利用改进的ShuffleNetv2模型从监控图像中提取内在特征.
- 应用黄金子优化 (GJO) 为ShuffleNetv2模型的最佳超参数调整.
- 使用长期短期记忆神经网络 (LSTM-NN) 进行最终的暴力检测分类.
主要成果:
- 在暴力检测任务中,DeepGuard模型表现出最佳的性能.
- 在基准数据集上达到99.00%和98.63%的高准确率.
- 在识别威胁的不同性能指标中,其表现优于其他现有技术.
结论:
- DeepGuard模型有效地使用DL和GJO的组合识别监控视频中的暴力.
- 拟议的系统提供了一种复杂而聪明的方法来增强传统的安全措施.
- 实时威胁识别能力有助于积极保护公共安全和关键基础设施.
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