Work

YOLOv11-Based UAV Foreign Object Detection for Power Transmission Lines

Year: 2025

Type: article

Abstract: Foreign object detection on transmission lines poses a significant threat to power grid security, while conventional manual inspection methods are inefficient and pose safety risks. To overcome the challenges of detecting foreign objects in complex environments, this paper proposes an enhanced YOLOv11_SDI detection framework with two key contributions. Firstly, a novel hierarchical Spatial-channel Dynamic Inference (SDI) module is integrated into YOLOv11, employing an adaptive feature fusion mechanism to enhance multi-scale representation. Secondly, a lightweight spatial attention unit is introduced to improve region-of-interest localization without compromising computational efficiency. In addition, the publicly available FOTL_Drone dataset is expanded to 5980 UAV images through systematic data augmentation, covering six critical foreign object categories. Comprehensive experiments validate the model’s superior performance, achieving state-of-the-art 95.2% [email protected] with only 3.74 M parameters, demonstrating its potential for practical transmission line inspection applications.

Source: Electronics

DOI: 10.3390/electronics14183577

Authors: Depeng Gao, Yihan Yin, Han Zhang, Changping Li, Bingshu Wang

Institutions: Nantong Institute of Technology, Xi'an University of Architecture and Technology, Northwestern Polytechnical University, Shenzhen University

Language: English

Topic: Power Line Inspection Robots

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896