通过注意力机制和完全卷积网络,提高了FCN注意力,通过注意力机制和完全卷积网络提高了青路面裂纹检测
Huiyuan Zhang1, Jiawei Liu2, Guoping Hu1
1School of Civil and Transportation Engineering, Henan University of Urban Construction, Longxiang Road, Xincheng Area, Pingdingshan, 467036, Henan, China.
Scientific reports
|July 12, 2025
概括
本研究引入了FCN注意模型用于青路面裂检测,实现高精度和精确的测量. 这种创新方法显著改进了道路表面分析的传统方法.
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:
- 青路面的恶化,特别是裂,带来了重大的安全和维护挑战.
- 准确而高效的裂检测对于及时的道路基础设施管理至关重要.
- 现有的方法往往缺乏详细裂分析所需的精度.
研究的目的:
- 开发和评估一个先进的深度学习模型,用于精确地检测青路面的裂.
- 通过使用集成的注意力机制来提高像素级别细分的准确性.
- 为提供裂尺寸 (长度和宽度) 的准确测量.
主要方法:
- 实现与注意力机制集成的完全卷积网络 (FCN) (FCN注意力模型).
- 使用基于ResNet-50的编码器,具有通道智能和空间注意模块,用于精细的特征提取.
- 与传统模型的性能进行比较,包括VGG-16,AlexNet,MobileNet和GoogleNet.
主要成果:
- FCN注意力模型的总准确率为90.79%,精度为92.3%,回忆率为89.5%,F1得分为90.9%.
- 实现了69.7%的欧盟交叉点 (IoU) 平均,每个图像的处理时间为109.1毫秒.
- 与基线模型相比,在裂长度和宽度计算方面表现出卓越的性能.
结论:
- 拟议的FCN注意力模型为青路面裂检测和分析提供了一个高度有效的解决方案.
- 注意力机制显著提高了模型专注于相关特征的能力,提高了细分精度.
- 该模型能够提供准确的裂尺寸,支持在道路维护和评估中的实际应用.
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