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Integrating Frequency-Spatial Features for Energy-Efficient OPGW Target Recognition in UAV-Assisted Mobile

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Summary

A new lightweight AI model, OPGW-DETR, accurately identifies Optical Fiber Composite Overhead Ground Wire (OPGW) cables during drone inspections. This ensures reliable power grid monitoring with minimal energy use.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Optical Fiber Composite Overhead Ground Wire (OPGW) cables are crucial for power system lightning protection and real-time grid monitoring communication.
  • Accurate identification of OPGW during Unmanned Aerial Vehicle (UAV) inspections is vital to prevent service disruptions and maintain functionality.
  • Detecting OPGW amidst visually similar wires presents challenges for low-power, edge-based UAV platforms due to computational and energy constraints.

Purpose of the Study:

  • To develop a lightweight and energy-efficient AI model for accurate OPGW detection on edge-based UAV platforms.
  • To address the limitations of UAVs in terms of battery life and bandwidth for continuous, real-time detection.
  • To improve the reliability of power grid monitoring by mitigating risks associated with OPGW misidentification.

Main Methods:

  • Proposed OPGW-DETR, a lightweight object detection model based on the D-FINE framework, optimized for low-power UAV operation.
  • Introduced multi-scale convolutional global average pooling (MC-GAP) to fuse multi-scale spatial features and spectrally motivated features.
  • Implemented a hybrid gating mechanism to dynamically balance global and spatial features while preserving information via residual connections.

Main Results:

  • The S-scale OPGW-DETR model demonstrated a 3.9% improvement in average precision (AP) and a 2.5% improvement in AP50 compared to the baseline.
  • The model enables real-time inference with minimal energy consumption, addressing UAV power and bandwidth limitations.
  • Achieved continuous detection capability essential for low-power UAV inspection scenarios.

Conclusions:

  • OPGW-DETR provides a viable solution for accurate OPGW identification in resource-constrained UAV inspection environments.
  • The improved detection accuracy enhances communication reliability and safeguards the power grid by reducing misidentification risks.
  • Enables uninterrupted grid monitoring, ensuring communication integrity in critical infrastructure management.