一个轻量级的YOLO11n seg框架,用于实时地表裂检测与细分
Shweta Tiwari1, Kamal Kumar Gola2, Rohit Kanauzia3
1Department of Computer Science and Engineering, Chandigarh University, Mohali, India. shweta.tiwari2006@gmail.com.
Scientific reports
|January 29, 2026
概括
本研究介绍了一种轻量级的深度学习模型,用于检测基础设施的表面裂. YOLO11n-seg模型提供快速而准确的裂识别,为自动化检查系统设定了新的基准.
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:
- 表面裂纹识别对于基础设施安全和寿命至关重要.
- 传统的方法依赖于手工检查和经典的图像处理.
- 深度学习为破裂检测提供了自动化解决方案.
研究的目的:
- 提出一个深度学习框架,用于自动地表裂检测.
- 评估YOLO11n-seg架构用于破解识别任务.
- 为边缘部署的裂识别系统建立一个基准.
主要方法:
- 使用Crack-Seg数据集进行培训和评估.
- 实现了YOLO11n-seg深度学习架构.
- 使用精度,盒子mAP@50和面具mAP@50指标评估模型性能.
主要成果:
- YOLO11n-seg模型的精度为78.8%,Box mAP@50的精度为76.2%.
- 轻量级模型 (280万个参数) 显示了58.7%的Mask mAP@50.
- 在特斯拉T4上实现了每张图像3.6ms的超快速推断速率.
结论:
- YOLO11n-seg架构为表面裂检测提供了强大的性能.
- 这种轻量级模型提供了可接受的细分性能与较低的计算成本.
- 这些发现为高效,边缘部署的裂纹识别建立了新的基准.
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