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Zero-Shot Learning-Based Detection of Electric Insulators in the Wild

EasyChair Preprint no. 5460

13 pagesDate: May 4, 2021


An electric insulator is an essential device for an electric power system. Therefore, maintenance of insulators on electric poles has vital importance. Unmanned Aerial Vehicle (UAV) are used to inspect conditions of electric insulators placed in remote and hostile terrains where human inspection is not possible. Insulators vary in terms of physical appearance and hence the insulator detection technology present in the UAV in principle should be able to identify a insulator device in the wild, even though it has never seen that particular type of insulator before. To address this problem a Zero-Shot Learning-based technique is proposed that can detect an insulator device type that it has never seen during the training phase. Different convolutional neural network models are used for feature extraction and are coupled with various signature attributes to detect an unseen insulator type. Experimental results show that inceptionsV3 has better performance on electric insulators dataset and basic signature attributes “Color and number of plates“ of the insulator is the best way to classify insulators dataset while the number of training classes doesn’t have much effect on performance. Encouraging results were obtained.

Keyphrases: electrical insulators, object detection, signature attribute, zero-shot learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ibraheem Azeem and Moayid Ali Zaidi},
  title = {Zero-Shot Learning-Based Detection of Electric Insulators  in the Wild},
  howpublished = {EasyChair Preprint no. 5460},

  year = {EasyChair, 2021}}
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