混动基于注意力的路面密封裂应急检测
Bo Yuan1, Zhaoyun Sun2, Lili Pei1
1School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710061, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一种新的基于注意力的混合方法可以通过使用先进的特征提取和智能交叉对欧盟损失函数来改进路面密封裂纹的检测. 这提高了路面的性能稳定性和准确性.
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:
- 路面的性能稳定性对于基础设施的寿命至关重要.
- 准确检测路面密封裂对于及时维护至关重要.
- 现有的方法可能缺乏复杂裂模式所需的精度.
研究的目的:
- 引入一种新的基于注意力的混合方法,用于路面密封裂纹检测.
- 为了提高识别路面裂的准确性和效率.
- 确保道路基础设施的长期稳定性和性能.
主要方法:
- 在特征提取和检测网络中集成混合注意模块.
- 使用具有多尺度特征融合的双向检测头.
- 在动态梯度调整中使用一个智能交叉点对欧盟损失函数.
主要成果:
- 获得了98.02%的平均平均精度 (mAP@0.5).
- 记录了0.9768的回忆和0.9680.1的F1得分.
- 与现有的一阶段最先进的方法相比,证明了卓越的性能.
结论:
- 提出的基于注意力的混合方法显著提高了路面密封裂纹检测的准确性.
- 在欧盟损失函数上的智能交叉改善了界限框的装配和覆盖范围.
- 这种方法为保持路面完整性和性能提供了一个有希望的解决方案.
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