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Open Access

ARTICLE

YOLOv10-HQGNN: A Hybrid Quantum Graph Learning Framework for Real-Time Faulty Insulator Detection

Nghia Dinh1, Vinh Truong Hoang1,*, Viet-Tuan Le1, Kiet Tran-Trung1, Ha Duong TTi Hong1, Bay Nguyen Van1, Hau Nguyen Trung1, Tien Ho Huong1, Kittikhun Meethongjan2,*
1 AI Lab, Faculty of Information Technology, Ho Chi Minh City Open University, 35-37 Ho Hao Hon Street, Co Giang Ward, District 1, Ho Chi Minh City, 700000, Vietnam
2 Department of Applied Science, Faculty of Science and Technology, Suan Sunandha Rajabhat University, 1 U-Thong Nok Rd, Dusit, Dusit District, Bangkok, 10300, Thailand
* Corresponding Authors: Vinh Truong Hoang. Email: vinh.th@ou.edu.vn or vinh.th@ou.edu.vn; Kittikhun Meethongjan. Email: kittikhun.me@ssru.ac.th or kittikhun.me@ssru.ac.th
(This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.069587

Received 26 June 2025; Accepted 30 October 2025; Published online 01 December 2025

Abstract

Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network (HQGNN). The YOLOv10 module, fine-tuned on domain-specific UAV datasets, improves detection precision, while the HQGNN ensures multi-object tracking and temporal consistency across video frames. This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions. Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics, achieving Recall of 0.85 and Average Precision (AP) of 0.83, with clear gains in both accuracy and throughput. These advancements support automated, proactive maintenance strategies that minimize downtime and contribute to a safer, smarter energy infrastructure.

Keywords

Object detection; GNN; QGNN; HQGNN; Quantum; YOLO; power quality

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