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基于YOLOv8-StarNet的赤脚足迹检测算法
Yujie Shen1,2, Xuemei Jiang2, Yabin Zhao1
1College of Investigation, People's Public Security University of China, Beijing 100000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种优化的赤脚足迹识别模型,该模型使用了YOLOv8.8中增强的StarNet架构. 该模型实现了99.5%的准确性,降低了参数和计算负载,以改善生物识别.
科学领域:
- 计算机视觉 计算机视觉
- 生物识别识别生物识别
- 机器学习 机器学习
背景情况:
- 传统的图像识别系统由于特征集中和丰富的纹理,难以处理赤脚脚印数据.
- 现有的方法在处理复杂的足迹模式时缺乏效率,用于安全和法医应用.
研究的目的:
- 为增强生物识别开发一个优化的足迹识别模型.
- 提高分析赤脚足迹图像的准确性和效率,用于安全,医疗和刑事调查.
主要方法:
- 将增强的StarNet架构集成到YOLOv8骨干中,以实现高效的功能提取.
- 使用元素智能乘法来映射输入到一个高维非线性特征空间.
- 采用一个编码器层,具有多级特征融合和注意力机制,用于语义信息提取.
- 实现特征调制块,以协同结合全球和本地信息,减少冗余和计算复杂性.
主要成果:
- 在专有裸足足迹数据集上实现了99.5%的识别准确度.
- 模型参数减少了0.73万,提高了处理速度.
- 将GFLOPS降低了1.5,降低了硬件性能要求.
- 在参数效率,识别精度和计算复杂度方面展示了显著的优势.
结论:
- 提议的增强型StarNet-YOLOv8模型为裸足足迹识别提供了一种轻量级且高度准确的解决方案.
- 该模型的效率和准确性使其适用于生物识别和法医学的现实应用.
- 未来的工作重点是扩大复杂的法医场景中的鞋印分析能力.
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