Data set of insulator defect detection based on UAV by YOLO

Citation Author(s):
Li
Benhao
Shanghai Dianji University
Submitted by:
Benhao Li
Last updated:
Fri, 01/03/2025 - 09:55
DOI:
10.21227/pt3w-rb35
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Abstract 

<p>The dataset forwas collected by UAVs equipped with camera heads to capture images of insulators on power transmission lines. These images have a resolution of 3872×2592 pixels. A total of 488 insulator defect images were selected, and the data was annotated using the LabelMe annotation software. This study's dataset annotated four types of labels: insulator, damaged, Flashover, and hammer. The insulator is a positive class label, and damaged, Flashover, and hammer are negative class labels.

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Instructions: 

<p>The original dataset is insufficient to meet the generalization and universality of the experiment. Therefore, the dataset was expanded by horizontally flipping, vertically flipping, and horizontally vertically flipping the images.</p>