基于YOLOv8-MNC的吸烟行为检测算法
Zhong Wang1,2, Lanfang Lei1, Peibei Shi2
1School of Artificial Intelligence and Big Data, Hefei University, Hefei, China.
Frontiers in computational neuroscience
|September 11, 2023
概括
一个新的YOLOv8-MNC算法通过解决小物体挑战来改善吸烟检测. 这种新的方法提高了识别烟的准确性和稳定性,推进了用于行为分析的计算机视觉.
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:
- 检测吸烟行为是具有挑战性的,因为小,隐藏的物体,如烟.
- 现有的深度学习模型在此类任务的准确性和稳定性方面扎.
研究的目的:
- 引入一种新的吸烟检测算法,YOLOv8-MNC,以克服当前深度学习方法的局限性.
- 为了提高在吸烟行为分析中检测小,隐藏物体的准确性和稳定性.
主要方法:
- 开发了YOLOv8-MNC,在YOLOv8的基础上构建了一个专门的小目标检测层.
- 集成的NWD损失通过减少对微小位置偏差的敏感性来提高训练准确性.
- 集成的多头自我注意机制 (MHSA) 增强了全球特征学习和CARAFE的高效上采样,以尽量减少特征损失.
主要成果:
- 在定制的吸烟行为数据集上,YOLOv8-MNC模型实现了85.887%的检测准确度.
- 与以前的算法相比,平均平均精度 (mAP@0.5) 显著增加了5.7%.
- 展示了对小,封闭物体的改进的检测精度和模型稳定性.
结论:
- YOLOv8-MNC在吸烟行为检测方面取得了重大进展,解决了准确性和稳健性的关键挑战.
- 该算法的增强性能表明其在相关的对象检测领域的潜在应用.
- 未来的工作重点是改进YOLOv8-MNC技术并探索其更广泛的应用.
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