# Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation

Source: [Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa71641e209131cea511dc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:05:39.468Z

Authors: Weixi Wang, Xichen Zhong, Xin Li, Sizhe Li, Xun Ma

Published: 2023-11-02T03:52:59.000Z

## Abstract

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 logical rules to categorize the type of defect. Experimental results indicate that this methodology focuses more on the equipment rather than the defects when identifying issues in overhead lines. This leads to a notable enhancement in accuracy and exhibits impressive adaptability. Thus, offering a fresh perspective for automating the inspection of distribution network equipment.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.00979>)

[PDF](<https://arxiv.org/pdf/2311.00979.pdf>)