基于电力传输线路故障检测提取更丰富的语义信息的新策略
Shuxia Yan1, Junhuan Li1, Jiachen Wang2
1School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的方法,用于使用浅深的深度学习网络在智能电网中有效检测缺陷. 该方法增强了轻量级设备上的语义特征提取,提高了输电线路检查的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 传统的输电线路缺陷检测是手动和有风险的.
- 智能电网需要使用无人机或机器人进行自动化,高效的缺陷检测.
- 深度学习提供了丰富的语义功能,但要求高计算资源,不适合轻量级设备.
研究的目的:
- 开发一种方法,在资源有限的设备上使用浅层网络提取先进的语义特征.
- 提高智能电网输电线路自动缺陷检测的准确性和效率.
- 为了能够准确地分类形态上相似的缺陷.
主要方法:
- 在预训练期间利用转移学习和自我监督学习来修改图像特征 (位置,边缘连接).
- 开发了一个类别的语义融合模块 (CSFM),用于增强功能融合.
- 采用浅层网络来提取下游任务的丰富语义特征.
主要成果:
- 拟议的方法在定制输电线路缺陷数据集上表现出卓越的性能.
- 实验表明,在预训练期间修改低级图像信息的有效性.
- 该策略在PASCAL VOC数据集上得到了很好的概括,并取得了与SFID数据集上最先进的方法可比的结果,但网络深度减少.
结论:
- 开发的方法可以在浅层网络中高效地提取丰富的语义特征,用于自动检测缺陷.
- 这种方法适用于智能电网应用中的轻量级嵌入式设备,如无人机和机器人.
- CSFM和修改后的预培训策略显著提高了缺陷检测能力,为智能电网基础设施提供了实用的解决方案.
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