在混凝土结构中强大的自动裂检测和细分的整体方法
Muhammad Sohaib1,2, Saima Jamil3, Jong-Myon Kim4,5
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
早期检测混凝土裂对于基础设施安全至关重要. 这项研究引入了一组量子化YOLOv8模型,用于稳健快速的裂检测和细分,改善实时维护.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 由于混凝土在关键基础设施中的广泛使用,因此需要早期检测裂,以防止不稳定.
- 传统的手动检查是耗时的,而现有的自动化方法在稳定性和速度方面扎.
研究的目的:
- 开发一种新,强大,高效的自动化系统,用于混凝土裂检测和细分.
- 解决当前基于人工智能的裂纹检测算法的关于复杂背景和推断时间的局限性.
主要方法:
- 开发了一个整体机制,结合了多个量子化You Only Look Once版本8 (YOLOv8) 模型.
- 整体方法合并了预测,以生成混凝土裂的最终细分面具.
主要成果:
- 拟议的模型实现了高分段性能,至少89.62%的精度和0.88.8的交叉与联合 (IoU) 评分.
- 每张图像的推断时间减少到27毫秒,比较模型的改善率为5%.
结论:
- 新型组合YOLOv8模型为混凝土裂检测和细分提供了一个强大的解决方案,具有较低的推理时间.
- 这种方法适用于实时应用,增强基础设施维护和安全协议.
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