ArticleSensors (Basel, Switzerland)2022
Insulator Umbrella Disc Shedding Detection in Foggy Weather.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A texture guided transmission line image enhancement method.Scientific reports · 2025Article
- Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images.Sensors (Basel, Switzerland) · 2024Article
- Deep Power Vision Technology and Intelligent Vision Sensors.Sensors (Basel, Switzerland) · 2023Article
- Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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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.
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