在复杂场景中使用YOLOv8和上下文感知卷曲的强大的面罩检测
Yingjie Wei1, Huili Li2, Yuanfei He1
1College of Information Engineering, Zhoukou Vocational College of Arts and Science, Zhoukou, 466000, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种先进的面罩检测算法,该算法专为具有挑战性的条件而设计,在复杂的环境中显著提高了准确性和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 面罩检测面临着诸如遮,照明变化和距离等挑战.
- 现有的算法在复杂环境中难以准确.
研究的目的:
- 为复杂环境开发一个强大的面罩检测算法.
- 为了提高检测准确度和实时性能.
主要方法:
- 构建了一个全面的面罩数据集.
- 通过深度可分离的卷积和SENet的注意力来增强YOLOv8架构.
- 整合了情境感知卷曲和DAM-Head,以改进特征提取和检测.
主要成果:
- 拟议的算法实现了98.11%的平均精度 (mAP).
- 记录的每秒 (FPS) 率为135.61. 这是记录的.
- 与主流算法相比,被证明具有更高的性能.
结论:
- 增强的算法有效地解决了面罩检测方面的挑战.
- 该方法为实际应用提供了高精度和实时功能.
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