对绝缘体自爆缺陷的轻量级检测方法
Yanping Chen1, Chong Deng1, Qiang Sun1
1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究介绍了Faster R-CNN-tiny,一种用于检测电网绝缘体缺陷的轻量级模型. 它实现了比传统方法更高的准确性和更快的速度,提高了电网安全.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在智能检查系统中,准确检测有缺陷的绝缘体对于电网安全至关重要.
- 传统的物体检测模型因参数大小,精度低,速度慢而受到影响.
研究的目的:
- 开发一种轻量级和高效的绝缘体缺陷检测模型.
- 为了提高实时电网检查的准确性和速度.
主要方法:
- 提出了一个轻量级的,更快速的基于区域的卷积网络 (更快的R-CNN-tiny) 模型.
- 在骨干中用EfficientNet取代ResNet,利用特征金字塔网络,并采用深度可分离的卷积.
- 实施转移学习与冷/解培训策略用于小缺陷检测.
主要成果:
- 与更快的R-CNN (ResNet) 相比,更快的R-CNN-tiny显示了明显改善的平均平均精度 (mAP) 和每秒 (FPS).
- 该模型实现了参数数量的大幅减少.
- 有效地提高了对小型绝缘体缺陷的检测.
结论:
- 更快的R-CNN-tiny为绝缘体缺陷检测提供了优质的解决方案,平衡精度,速度和模型大小.
- 拟议的模型提高了智能电气系统检查的安全性和效率.
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