Insulator Defect Detection based on Faster R-CNN and YOLOv3 Algorithm
ID：111
Submission ID：5 View Protection：ATTENDEE
Updated Time：2023-11-20 13:53:17 Hits：124
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Abstract
In order to solve the problem that the existing insulator defect detection models are not effective enough for the tasks such as complex background and low signal-to-noise ratio of the target objects, this paper proposes an insulator defect detection method based on Faster R-CNN and YOLOv3 target detection algorithm. First, operations such as random angle rotation, flipping, adjusting contrast, and adding noise are used to preprocess the data images. Secondly, the Faster R-CNN algorithm is used as the basis to realize the accurate localization of insulators. Finally, EfficientNet-B3 is used as the backbone network of YOLOv3, and the dual-attention mechanism CBAM is embedded to realize the insulator defect recognition. The results show that compared with the existing models, the insulator defect detection method proposed in this paper exhibits more accurate insulator localization and defect recognition performance.
Keywords
Insulator; Faster R-CNN; YOLOv3; EfficientNet-B3; CBAM
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