轻量级双重注意网络用于混凝土裂分割
1Anhui Provincial International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures, Chuzhou 239099, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了一种轻量级的双重注意网络,用于边缘设备上的精确裂纹细分. 该模型以最小的计算成本实现高性能,可实现实时民用基础设施检查.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 结构健康监测需要在资源有限的边缘设备上进行准确的裂细分.
- 现有的模型往往超过了这些设备的功率,内存和延迟限制.
研究的目的:
- 为边缘设备开发一个轻量级的裂细分网络.
- 在严格的资源限制下,实现与重量级模型可比的高精度.
主要方法:
- 开发了一个压缩的U-Net架构 (轻量级的双重注意力网络).
- 集成了一个紧的双重注意力块,结合了通道和空间自我注意力.
- 该网络被优化为低功耗和INT8量子化.
主要成果:
- 该网络在混凝土裂纹基准测试中实现了0.827的IoU和0.905的F1得分.
- 它的表现优于现有的轻量级模型,如MobileNetV3和ESPNetv2.
- 实时性能在Jetson Nano上的110 FPS和Coral TPU上的220 FPS时得到了验证.
结论:
- 轻量级的双重注意网络为设备上民用基础设施检查提供了尖端的裂细分.
- 双重注意模块是模型准确性和效率的关键.
- 该模型满足了在资源有限的环境中实时监控的需求.
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