基于CBG-YOLOv5s的金属表面腐蚀的检测和识别
Mingjiao Fu1, Zhitong Jia1, Lingzhi Wu2
1School of Computer and Control Engineering, Yantai University, Yantai, China.
PloS one
|April 10, 2024
概括
这项研究引入了一种新的CBG-YOLOv5s模型,用于检测金属表面腐蚀,显著提高沿海结构的精度和速度. 先进的模型增强了特征表示和小目标检测,以更好地进行结构安全评估.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
背景情况:
- 评估金属结构中的结构损伤和安全性需要精确检测表面腐蚀.
- 沿海金属设施特别容易受到腐蚀的影响,需要有效的监测方法.
研究的目的:
- 提出一个先进的物体检测模型,CBG-YOLOv5s,用于准确和高效的金属表面腐蚀检测.
- 通过整合注意力机制和多尺度特征融合来提高YOLOv5s模型的性能.
主要方法:
- 通过整合卷积块注意模块 (CBAM) 来开发C3CBAM模块,以改善特征表示.
- 引入了多级特征融合和一个小型目标检测层,以增强检测能力.
- 设计了一个更轻的C3Ghost模块,以减少模型参数和计算负载,增加运行速度.
主要成果:
- 拟议的CBG-YOLOv5s模型与标准YOLOv5s和其他物体检测模型相比,表现出更高的性能.
- 在6000张腐蚀图像的定制数据集上的实验证实了检测准确度和速度的改进.
- 改进后的模型有效地改善了通道和空间注意力,特征表示和小目标检测.
结论:
- CBG-YOLOv5s型号为自动金属表面腐蚀检测提供了一个高度有效的解决方案.
- 集成CBAM,多尺度融合和C3Ghost模块显著提高了检测准确性和操作速度.
- 这项研究有助于改善金属基础设施的结构健康监测和安全评估,特别是在沿海环境中.
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