# Energy-Efficient Automated Detection of OPGW Features for Sustainable UAV-Based Inspection

## TL;DR

This paper introduces an energy-efficient model for detecting small features in power line inspections using drones, improving accuracy and reducing computational needs.

## Contribution

The paper proposes a novel detection model with a Feature Enhancement Module and a new loss function for improved small-object detection in UAV inspections.

## Key findings

- The model achieves a mAP50 of 78.3% and mAP50–95 of 52.0%, outperforming the baseline.
- The model offers high detection accuracy with low computational resource requirements.
- The use of Normalized Wasserstein Distance loss improves boundary regression for small objects.

## Abstract

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a mAP50 of 78.3% and mAP50–95 of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections.