基于知识蒸的道面的轻量级缺陷检测算法.
Anfu Zhu1, Jiaxiao Xie1, Bin Wang1
1North China University of Water Resources and Electric Power, Zhengzhou, China.
Scientific reports
|November 7, 2024
概括
本研究介绍了一种轻量级算法,用于使用知识蒸检测道层缺陷. 改进的模型显著减少了尺寸,同时提高了实时缺陷识别的准确性.
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:
- 道层缺陷,如脱洞和不充分的紧缩,是由于施工质量,地质和水文学的原因造成的.
- 目前的检测方法受困于复杂的模型,实时性能差,精度低.
研究的目的:
- 开发一种轻量级且准确的道层缺陷检测算法.
- 解决现有的复杂和缓慢检测模型的局限性.
主要方法:
- 使用C3CSFM,MDFPN和RWNMS模块构建了一个高精度的教师模型 (YOLOv5s).
- 采用知识蒸,化特征和输出尺寸以获得准确性.
- 学习的面具具有空间和通道维度的关系,用于实时检测.
主要成果:
- 模型参数减少了80% (从16.03 MB减少到3.20 MB).
- 平均精度从83.4%提高到86.5% (增加了3.1%).
- 实现了一种轻量级的模型,同时保持了检测性能.
结论:
- 拟议的轻量级算法允许高精度和实时检测道层缺陷.
- 知识蒸有效地降低了模型的复杂性,同时提高了准确性和速度.
相关概念视频
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