使用改进的YOLOv11模型来增强计算机视觉应用程序,用于构建裂检测算法
Xiaohu Gao1,2, Chunmei Cao3,4, Xiaojing Yi5
1School of Electronics and Information, Jiangsu Vocational College of Business, Nantong, 226011, China. gaoxiaohu1979@163.com.
Scientific reports
|November 6, 2025
概括
这项研究增强了用于构建裂检测的YOLOv11算法,达到88.6%的准确性. 改进的模型为识别各种裂类型提供了更高的精度和回忆力,确保了结构安全.
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:
- 城市化增加了对高效结构安全监测的需求.
- 传统的裂检测方法效率低,错误率高.
- 深度学习,特别是YOLO算法,显示了自动裂检测的前景.
研究的目的:
- 开发一个增强的YOLOv11模型,以提高建筑裂检测准确度和实时性能.
- 为了解决检测小型和复杂裂纹模式的局限性.
主要方法:
- 引入了C3K2-SG模块,用于复杂的背景裂特征提取.
- 实现了FPSConv模块,用于多尺度裂检测.
- 利用了Inner_MPDIoU损失函数来增强小裂的定位.
主要成果:
- 实现了88.6%的检测准确度 (mAP@0.5),比YOLOv11.11提高了4.6%.
- 精度提高了3.5%,回忆率提高了7.6%.
- 在检测小目标和复杂的裂类型方面表现出卓越的性能.
结论:
- 增强的YOLOv11模型为建筑裂检测提供了高效和准确的解决方案.
- 该模型显示了智能检查和结构安全监控的显著优势.
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