一个优化的YOLOv11框架,用于高效的混凝土表面多类别缺陷检测
Zhuang Tian1, Fan Yang1,2, Lei Yang1
1School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
一个新的混凝土缺陷检测模型,YOLOv11-EMC,提高了准确性,并减少了识别结构缺陷的错误. 这提高了结构安全性,并延长了混凝土基础设施的使用寿命.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的混凝土缺陷识别对于结构安全和寿命至关重要.
- 现有的模型难以应对具体缺陷的复杂性和可变性,导致检测不准确.
- 这些限制在现实工程应用中对结构完整性构成风险.
研究的目的:
- 开发一个高效和强大的混凝土缺陷检测模型.
- 提高现有检测模型的准确性和概括性.
- 解决工程检查中不规则的缺陷形状和复杂性所带来的挑战.
主要方法:
- 拟议的YOLOv11-EMC (高效的多类别混凝土缺陷检测) 模型.
- 嵌入了修改后的可变形卷积,以有效地提取不规则的缺陷特征.
- 集成的C3k2模块与修订的动态卷积模块用于计算效率和改进的特征表示.
主要成果:
- 与Yolov11相比,YOLOv11-EMC在精度 (+8.3%),回忆 (+2.1%),mAP50 (+4.3%) 和F1 (+3%) 中显著改善.
- 使用无人机图像的实地测试证实了减少虚假和不足的检测.
- 该模型在实际工程场景中实现了更高的总体检测精度.
结论:
- YOLOv11-EMC提供了一种卓越的方法来识别混凝土结构中的有形缺陷.
- 改进后的模型提高了具体缺陷检测的稳定性和通用性.
- 这有助于提高工程应用中的结构安全性和延长使用寿命.
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