校园风险检测使用S-YOLOv10-SIC网络和自校准照明算法
Qiang Zhao1, Sha Liu1, Shihao Zhang2,3
1Wuhan Donghu University, Wuhan, 430071, China.
Scientific reports
|July 7, 2025
概括
这项研究引入了一种改进的YOLOv10算法,用于智能校园风险检测,在低光条件下提高准确性. 优化的模型显著降低了计算负载,并改善了检测指标,为更智能的校园开发铺平了道路.
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:
- 校园安全需要准确高效的风险检测系统.
- 现有的物体检测模型在低光环境和计算效率方面面临着挑战.
研究的目的:
- 为智能校园风险检测开发一个增强的YOLOv10算法.
- 为了提高检测准确度,特别是在低光条件下,并减少计算复杂性.
主要方法:
- 集成一个自我校准的照明算法与YOLOv10.
- 使用辅助界限框优化损失函数.
- 通过StarNet和卷积块注意模块增强网络结构.
主要成果:
- 降低了约20%的分类损失和约16%的特征点损失.
- 参数,梯度和GFLOP降低了80%以上.
- 改进了精度 (+0.99%),召回 (+3.31%),F1 (+2.15%) 和mAP (+1.23%) 的提升.
结论:
- 改进的YOLOv10算法在校园风险检测方面表现出卓越的性能,特别是在低光场景中.
- 该模型在效率和准确性方面提供了显著的改进,支持开发更智能的校园环境.
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