ConvNeXt钢砂替代率检测方法包括注意力机制
Shengjie Teng1, Lin Zhu1, Yunze Li2
1College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Ürümqi, 830052, China.
Scientific reports
|June 30, 2023
概括
一种新的深度学习方法可以准确地检测混凝土中的钢砂替代率. 这种方法通过改善现有的检测低效率来提高体积稳定性评估.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
背景情况:
- 钢渣混凝土的体积稳定性受钢渣砂代替天然沙的比例的影响.
- 目前用于检测钢替代率的方法效率低下,缺乏代表性抽样.
研究的目的:
- 提出基于深度学习的方法,以准确有效地检测钢砂替代率.
- 提高钢砂检测模型的特征提取和预测准确度.
主要方法:
- 为了提取颜色特征,开发了一种集成挤压和激发 (SE) 注意力机制的ConvNeXt模型.
- 使用迁移学习来进一步提高模型在预测钢砂替代率方面的准确性.
主要成果:
- 在预测钢砂替代率方面,SE-ConvNeXt模型取得了87.99%的准确性.
- 使用迁移学习提高了模型的准确性,达到92.64%,增加了4.65%.
结论:
- 拟议的深度学习方法,包括SE注意力和迁移学习,显著提高了钢砂替代率检测的准确性和效率.
- 该技术为识别钢砂含量提供了快速而精确的解决方案,这对于确保钢混凝土体积稳定性至关重要.
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