Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation. (arXiv:2311.00979v1 [cs.CV])

Overhead line inspection greatly benefits from defect recognition using
visible light imagery. Addressing the limitations of existing feature
extraction techniques and the heavy data dependency of deep learning
approaches, this paper introduces a novel defect recognition framework. This is
built on the Faster RCNN network and complemented by unsupervised semantic
segmentation. The approach involves identifying the type and location of the
target equipment, utilizing semantic segmentation to differentiate between the
device and its backdrop, and finally employing similarity measures and …

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