一种强大的自我监督的方法,用于在混凝土结构中检测细粒度裂.
Muhammad Sohaib1,2, Md Junayed Hasan3, Mohd Asif Shah4,5
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
Scientific reports
|June 2, 2024
概括
本研究介绍了一种自我监督的YOLOv8 (SS-YOLO) 方法,用于检测混凝土结构中的细粒度裂. 该方法提高了裂检测和细分的准确性,改善了结构完整性的评估.
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:
- 混凝土结构容易因细粒度裂而恶化,损害结构完整性.
- 现有的计算机视觉方法,包括深层卷积神经网络和变压器,在精确地定位这些微小裂方面存在困难.
- 自动裂检测对于及时维护和延长混凝土基础设施的寿命至关重要.
研究的目的:
- 开发一种先进的计算机视觉方法,用于准确检测和细分混凝土结构中的细粒度裂.
- 改进现有方法,这些方法在局部化微妙裂纹特征方面面临挑战.
- 提高自动化结构健康监测系统的效率和准确性.
主要方法:
- 一种基于YOLOv8模型的新型自我监督的"你只看一次" (SS-YOLO) 方法.
- 集成卷积块注意力 (CBAM) 和高斯适应重量分布多头自我注意 (GAWD-MHSA) 模块,以增强特征识别.
- 基于课程学习的自我监督伪标签 (CL-SSPL) 的应用,以提高有限数据集的性能.
主要成果:
- 实现了超过90.01%的平均精度 (mAP) 和87%的F1得分.
- 在三个数据集中,与现有方法相比,在mAP中至少有2.62%的显著改善,在F1得分中至少有4.40%的显著改善.
- 每张图像的推断时间减少了2毫秒,表明效率提高.
结论:
- 拟议的SS-YOLO方法有效地解决了在混凝土结构中细粒度裂检测和细分的挑战.
- 注意力机制和伪标签的整合大大提高了检测的准确性和稳定性.
- 这种方法为自动化结构健康监测提供了一个有希望的解决方案,提高了混凝土基础设施的可靠性和寿命.
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