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雾天中绝缘子伞裙脱落检测。

Insulator Umbrella Disc Shedding Detection in Foggy Weather.

机构信息

State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China.

Department of Automation, North China Electric Power University, Baoding 071003, China.

出版信息

Sensors (Basel). 2022 Jun 28;22(13):4871. doi: 10.3390/s22134871.

Abstract

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network's attention to the details of small targets and improve the model's ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.

摘要

绝缘子伞盘脱落的检测对输电线路的稳定运行非常重要。为了在雾天准确检测绝缘子伞盘脱落,提出了一种结合去雾算法的两级检测模型。在绝缘子图像去雾阶段,解决真实雾天图像数据的问题比较困难;利用合成雾天图像训练和真实雾天图像微调的方法对雾天图像进行去雾。在伞盘脱落检测阶段,提出了一种名为 FA-SSD 的小目标检测算法,以解决伞盘脱落仅占航拍图像很小比例的问题。一方面,融合浅层特征信息和深层特征信息,提高小目标的特征提取能力;另一方面,引入注意力机制,增强特征提取网络对小目标细节的关注,提高模型检测伞盘脱落的能力。实验结果表明,所提模型能够准确检测雾天图像中的绝缘子伞盘脱落缺陷;缺陷检测的准确率为 0.925,召回率为 0.841。与原始模型相比,分别提高了 5.9%和 8.6%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83ed/9269560/0e6de62d44e1/sensors-22-04871-g001.jpg

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