Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.082430
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

High-Performance and Lightweight Detection Network for Substation Equipment Defect Detection

Hongliang Tian, Xiaoke Liu*, Bolin Song, Chenying Pei
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, China
* Corresponding Author: Xiaoke Liu. Email: email
(This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies, 2nd Edition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082430

Received 16 March 2026; Accepted 26 May 2026; Published online 12 June 2026

Abstract

In the intelligent inspection of power systems, the detection of equipment defects is confronted with problems such as low background discrimination, multi-scale morphological differences, and the difficulty in identifying small targets and fine-grained defects, which makes it hard for existing models to balance detection accuracy and computational efficiency. To address this, this study proposes an improved lightweight detection framework, GRID-YOLO. This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module (C2MSA), designs an enhanced multi-scale bidirectional feature pyramid network (EMFPN) to achieve efficient alignment and preservation of cross-level features, and builds a hybrid attention reparameterization fusion module (HAFusion) to dynamically filter and enhance heterogeneous features. Additionally, a grouped distribution focal loss head (GDFLHead) is adopted to strengthen the collaborative optimization between classification and regression tasks. In this study, a dedicated dataset for substation equipment defect detection was constructed, and extensive comparative experiments were conducted. The results show that GRID-YOLO achieves an mAP50 of 78.3%, which is 6.1 percentage points higher than the baseline model. The mAP50–95, Recall, and F1-score reach 52.3%, 73.6%, and 77.6%, respectively, and the inference speed reaches 506 FPS. Compared with various advanced detection models, GRID-YOLO demonstrates strong overall performance and can better meet the actual needs of substation equipment defect detection in the intelligent inspection scenarios of power systems.

Keywords

Substation equipment defect detection; hybrid attention; multi-scale feature fusion; GRID-YOLO

  • 195

    View

  • 40

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link