基于光的混凝土沉降探测-AWGAM-YOLOv8nn
Yongxing Hao1, Bin Wang1, Wei Xiao2
1School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China.
Scientific reports
|November 17, 2025
概括
这项研究通过使用改进的YOLOv8n算法和添加重量全球注意力机制 (AWGAM) 来增强混凝土砂沉降检测. 优化的模型实现了更高的精度和效率,用于智能混凝土混合.
科学领域:
- 建筑工程工程 建筑工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 精确的混凝土砂沉降检测对于智能混凝土混合至关重要,影响项目质量和施工效率.
- 现有的方法可能缺乏实时应用所需的效率和精度.
- YOLOv8n算法为对象检测提供了基础,但需要针对特定任务进行优化.
研究的目的:
- 为了提高混凝土砂沉降检测的效率和精度.
- 增强YOLOv8n算法,以实现更好的功能融合和减少计算负载.
- 为了在智能建筑中平衡检测精度和计算效率.
主要方法:
- 集成增重全球注意力机制 (AWGAM) 与YOLOv8n子和骨干网络中的C2f模块 (基本和自适应版本).
- 在AWGAM中引入深度可分离的卷积,为骨干创建一个轻量级模块 (Light-AWGAM).
- 部署了增强模块,以提高多尺度特征融合和模型效率.
主要成果:
- 改进的YOLOv8n模型显示精度增加了3.3%,回忆率增加了0.6%.
- 平均精度 (mAP50和mAP50-95) 显示了0.8%的改善.
- 该模型实现了参数的显著减少,同时保持了高的检测精度和计算效率.
结论:
- 增强的YOLOv8n算法与AWGAM显著改善了混凝土砂沉降检测.
- 拟议的轻量级模块和注意力机制有效地平衡了准确性和效率.
- 这种优化的模型非常适合于智能混凝土混合中的高效和精确的检测任务.
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