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A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data.

Jie Zhang1, Xinyan Qin1, Jin Lei1

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

A new auto-synthesis dataset approach uses prior data to create synthetic images for power transmission line fitting recognition. This method significantly reduces costs while achieving high accuracy comparable to real-world data.

Keywords:
BlenderYOLOXfitting recognitioninspection robotprior series datasynthesis dataset

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Electrical Engineering

Background:

  • Data collection for power transmission line (PTL) fitting recognition in complex environments is challenging and expensive.
  • Existing methods often require extensive real-world data, limiting scalability and efficiency.

Purpose of the Study:

  • To propose a novel auto-synthesis dataset approach for PTL fitting recognition.
  • To reduce the cost and difficulty associated with data collection for PTL inspection.

Main Methods:

  • Formulating synthesis rules from prior series data.
  • Rendering 2D images using virtual 3D techniques based on synthesis rules.
  • Generating synthetic datasets with annotations using OpenCV image processing.

Main Results:

  • A synthetic dataset was generated and used to train a recognition model.
  • The trained model achieved a mean average precision (mAP) of 0.98 on a real dataset.
  • Recognition accuracy was comparable to models trained on real samples.

Conclusions:

  • The auto-synthesis dataset approach is feasible and effective for PTL fitting recognition.
  • This method significantly reduces the cost of dataset generation.
  • The approach enhances dataset establishment efficiency, providing a basis for deep learning models.