GCS-YOLOv8:一个轻量级的面部提取器,以协助深度假冒检测
Ruifang Zhang1,2, Bohan Deng1,2, Xiaohui Cheng3
1Key Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin University of Technology, Guilin 541006, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
我们开发了GCS-YOLOv8,一种轻量级的面部提取器,用于深度假冒检测. 这种模型提高了准确性,降低了计算成本,使得深度假冒分析更加高效和有效.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 数字法医学数字法医学
背景情况:
- 深度假冒视频带来了诸如模糊特征和由于压缩导致的错误检测等挑战.
- 现有的面部提取方法通常需要大量的计算资源,这阻碍了实际应用.
研究的目的:
- 提出GCS-YOLOv8,一个轻量级和高效的面部提取器,以帮助深度假冒检测.
- 为了提高在压缩的Deepfake视频中检测面部的准确性,同时尽量减少计算需求.
主要方法:
- 实现了HGStem模块用于初始下方采样,以减少小物体的错误检测.
- 引入了C2f-GDConv模块和P6大型目标检测层,以优化参数和扩大受体场.
- 设计了一个跨尺度特征融合模块 (CCFG),并改进了检测头,使用组正常化和共享卷积.
- 通过删除低质量的标签来改进培训数据集,以降低虚假检测率.
主要成果:
- 在WiderFace数据集上,GCS-YOLOv8实现了更好的平均精度 (AP):94.2% (简单),92.7% (中等) 和81.2% (难度).
- 与YOLOv8.8相比,该模型的参数显著减少 (1.68 MB,减少44.2%) 和FLOP (3.5 G,减少56.8%).
- 证明了对尺度变化的增强适应性和在低压缩深度假冒中更好地检测大规模面部.
结论:
- 在深度假冒检测中,GCS-YOLOv8提供了一种轻量级且有效的面部提取解决方案.
- 提出的方法成功地解决了模糊特征,错误检测和高计算成本的挑战.
- 这个模型为深度假冒分析提供了一个更有效,更准确的工具.
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