一个轻量级的卷积神经网络架构,用于在视频序列中检测暴力
Bhawana Tyagi1, Richa Jain2, Pankaj Jain3
1School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India. bhawana1988@gmail.com.
Scientific reports
|February 6, 2026
概括
这项研究介绍了一种轻量级的深卷积神经网络 (CNN),用于在公共空间实时检测暴力. 优化的模型在基准数据集上实现了高精度,同时大大降低了用于实际监控应用的计算负载.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在公共场所不断升级的暴力事件需要有效的实时检测系统.
- 处理用于暴力检测的高维视频数据是计算密集和复杂的.
- 现有的方法与时空变化和照明不一致性作斗争.
研究的目的:
- 开发一个计算效率高,准确的实时暴力检测框架.
- 为了显著减少计算开销而不会影响分类准确性.
- 为了实现在资源有限的硬件上部署,用于现实世界的监控.
主要方法:
- 开发了一个基于MobileNetV2.2的轻量级深卷积神经网络 (CNN) 架构.
- 优化了CNN使用深度可分离卷积和逆转剩余瓶.
- 预处理的视频 (224x224分辨率,规范化,增强) 以提高一般化.
- 训练并评估了现实生活暴力情况数据集 (RLVSD) 和曲棍球战斗数据集 (HFD) 的模型.
主要成果:
- 在RLVSD上达到97%的精度,在HFD上达到94%的精度.
- 与传统的CNN架构相比,显示出更高的精度,回忆和F1分数.
- 证实了实质性的效率改进,使资源有限的硬件能够实时推断.
结论:
- 优化的轻量级CNN架构可以在降低计算成本的情况下实现高精度的暴力检测.
- 拟议的框架非常适用于现实世界监控系统.
- 未来的工作将探索时间特征集成和跨领域的适应性.
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