LBA-YOLO:一种新的轻量化方法,用于检测建筑结构中的微裂
1School of Civil Engineering, Inner Mongolia University of Technology, Hohhot City, China.
PloS one
|May 9, 2025
概括
本研究介绍了一种基于YOLOv8n的先进算法,用于构建裂检测,提高识别结构缺陷的准确性和效率. 这种新的方法增强了特征提取和注意力机制,以便在复杂的环境中可靠地识别微裂.
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:
- 精确的建筑裂检测对于结构完整性和安全至关重要.
- 挑战包括不同的裂大小和不一致的数据集,阻碍精确的定位.
- 现有的方法难以实时,高精度检测微裂.
研究的目的:
- 开发一种高效准确的算法,用于检测建筑裂,特别是微裂.
- 增强模型处理复杂背景的能力,并改进语义细分.
- 为了减少计算开销,同时保持高检测性能.
主要方法:
- 使用YOLOv8n架构作为基本模型.
- 引入了AC-LayeringNetV2,一个层次的骨干,用于优化功能提取 (本地,外围,全球上下文).
- 集成RAK-Conv,该模块结合了注意力和不规则的卷积,用于复杂的背景处理.
主要成果:
- 实现了2.20%的精度提升和3.50%的回忆提升.
- 与基线模型相比,mAP@50的1.90%上升.
- 模型尺寸减少了6.55%,计算复杂度减少了0.03%.
结论:
- 拟议的模型为建筑物中的自动裂检测提供了实际适用性和效率.
- 功能融合和注意力机制的新型集成有效地解决了实时检测挑战.
- 该方法显示了通过先进的微裂识别来提高结构安全的巨大潜力.
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