A Training-Free Foreground–Background Separation-Based Wire Extraction Method for Large-Format Transmission Line Images

TL;DR

This paper introduces a method to extract transmission wires from large images without needing training data, using depth estimation and line detection.

Contribution

A novel training-free wire extraction method using depth maps and line segments for large transmission line images.

Findings

The method effectively separates wires from complex backgrounds using depth estimation.

It outperforms deep learning methods by being training-free and dataset-independent.

Experimental results show reduced computational overhead and improved background handling.

Abstract

With the rapid development of smart grids, deep power vision technologies are playing a vital role in monitoring the condition of transmission lines. In particular, for high-resolution and large-format transmission line images acquired during routine inspections, accurate extraction of transmission wires is crucial for efficient and accurate subsequent defect detection. In this paper, we propose a training-free (i.e., requiring no task-specific training or annotated datasets for wire extraction) wire extraction method specifically designed for large-scale transmission line images with complex backgrounds. The core idea is to leverage depth estimation maps to enhance the separation between foreground wires and complex backgrounds. This improved separability enables robust identification of slender wire structures in visually cluttered scenes. Building on this, a line segment structure-based method is developed, which identifies wire regions by detecting horizontally oriented linear features while effectively suppressing background interference. Unlike deep learning-based methods, the proposed method is training-free and dataset-independent. Experimental results show that our method effectively addresses background complexity and computational overhead in large-scale transmission line image processing.

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