基于改进的YOLOv8的混凝土表面裂纹检测算法
Xuwei Dong1, Yang Liu1, Jinpeng Dai2
1Key Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究介绍了YOLOv8-CD,这是一种改进的轻量级算法,用于检测混凝土裂. 它提高了基础设施安全的准确性和效率,提供实时监控功能.
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:
- 混凝土基础设施的安全依赖于准确的裂检测,以便及时修复.
- 现有的方法面临诸如高成本,处理速度慢,移动应用有限等挑战.
- 开发高效准确的自动裂检测对于结构健康监测至关重要.
研究的目的:
- 提出一个改进的轻量级算法,YOLOv8-裂纹检测 (YOLOv8-CD),用于增强混凝土表面裂纹检测.
- 通过提高准确性,速度和计算效率来解决现有方法的局限性.
- 调整先进的深度学习模块,以有效地提取裂纹特征.
主要方法:
- 开发了一个改进的YOLOv8架构 (YOLOv8-CD),集成视觉注意网络 (VAN) 和大卷积注意 (LCA) 模块.
- 引入了一种新的大型可分离内核注意力 (LSKA) 模块,以提取裂特定的特征.
- 优化包括在骨干中使用Ghost模块和在子中使用VoV-GSCSP的GSConv,以减少计算复杂性.
主要成果:
- 在RDD2022和Wall Crack数据集上,YOLOv8-CD算法在平均平均精度 (mAP50和mAP50-95) 中显著改善.
- 实现了较低的计算负载 (7.9 × 10 ^ 9) 和88 FPS的检测速度,以实现实时性能.
- 在混凝土裂检测任务中表现优于其他主流物体检测算法.
结论:
- 拟议的YOLOv8-CD算法为混凝土表面裂检测提供了优越,轻量级和高效的解决方案.
- 集成LSKA,Ghost模块和GSConv有效地增强了功能提取并降低了计算成本.
- 这种方法使得实时,准确的监控对于基础设施安全和维护至关重要.
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