Deep Learning-Based Visual Analytics for Efficiency and Safety Optimization in Power Infrastructure

Ссылка для цитирования (ENG)

Afanaseva Olga Vladimirovna , Tulyakov T. F., Shaimardanov A. A. Deep Learning-Based Visual Analytics for Efficiency and Safety Optimization in Power Infrastructure Eng. 2026. №7. pp. 1-32. https://www.mdpi.com/2673-4117/7/3/135

Авторы

Afanaseva Olga Vladimirovna , Tulyakov T. F., Shaimardanov A. A.

Журнал

Eng

Год

2026

Ключевые слова

Аннотация

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazard-ous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best perfor-mance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems ena-bles a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure.