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精度-效率权衡:优化YOLOv8用于结构裂纹检测
Jiahui Zhang1, Zoia Vladimirovna Beliaeva1, Yue Huang1
1Institute of Civil Engineering and Architecture, Ural Federal University, St. Mira19, 620002 Yekaterinburg, Russia.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究优化了YOLOv8模型用于结构裂检测,提高了准确性和效率. 改进的模型在实时工程应用中提供更快,更精确的细裂识别.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 结构健康监测 结构健康监测
背景情况:
- 对于结构裂检测的深度学习模型面临着准确性-效率的权衡.
- 实时对象检测算法,如YOLO (你只看一次) 提供速度,但需要优化复杂的特征表示.
研究的目的:
- 开发一个优化的YOLOv8模型,以提高结构裂检测的准确性和效率.
- 为了提高细裂的检测,同时保持实时性能.
主要方法:
- 增强了YOLOv8骨干与SimAM注意力机制,以改善裂特征表示.
- 整合了一个轻量级的C3Ghost模块,以减少模型参数和计算.
- 用双向多尺度特征融合结构取代标准子,以提高效率.
主要成果:
- 在0.5 IoU时达到88.7%的平均平均精度 (mAP),在mAP@0.5:0.95.4时达到69.4%.
- 减少了12.3%的计算成本,减少了千兆浮点操作 (GFlops).
- 与原始模型相比,演示了更快的推断速度.
结论:
- 优化的YOLOv8模型有效地平衡了结构裂检测的准确性和效率.
- 提议的改进可以在实时场景中更好地检测细裂.
- 该模型非常适合实际的工程应用,需要快速准确的结构评估.
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