Work

IDD-DETR: Lightweight Multi-Defect Detection Model for Transmission Line Insulators Based on UAV Remote Sensing

Year: 2026

Type: article

Abstract: Aiming to address the challenges of excessive model parameters, high computational complexity, strong complex background interference, and inadequate small-target detection found in insulator defect detection when using UAV remote sensing imagery of transmission lines, we propose a lightweight multi-defect detection model—Insulator Defect Detection-DETR (IDD-DETR). Specifically, we introduce a lightweight multi-starblock feature extractor (LMS-FE) as the backbone network to enhance its feature extraction capacity. Next, in order to enhance small-defect detection performance, a multi-scale feature pyramid (SOEP) is constructed by integrating shallow high-resolution features into the neck network. Additionally, a lightweight multi-branch feature fusion module (LMB-FF) is designed to efficiently fuse spatial and semantic information of small defects, suppressing background interference while optimizing model complexity. Finally, experimental results demonstrate that IDD-DETR achieves a 2.2% improvement in mean average precision (mAP) on the insulator small-defect dataset compared with the baseline algorithm, with model parameters and computation reduced by 44.9% and 47.1%, respectively. It also reaches a detection speed of 61.2 frames per second, satisfying the lightweight and high-precision requirements for edge deployment in transmission line inspection scenarios.

Source: Remote Sensing

DOI: 10.3390/rs18030486

Authors: Cheng Xu, Xin Liu, Jiaxin Wang, Yun Ding, Chunhou Zheng

Institutions: Anhui University, North China Electric Power 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: $3,258