Introduction:
Electrical distribution networks often traverse remote or hazardous terrains, making conventional ground-based inspections both risky and inefficient. Recent advances in UAV technology and AI-based computer vision have opened new avenues for remote asset monitoring (Shi et al., 2022). In this study, we introduce Powerline AI, an integrated system leveraging drones and object detection to automate powerline inspection tasks.

Methods:
Using drone-mounted high-resolution cameras, field images are captured from previously inaccessible areas. A deep learning-based object detection module, trained on annotated electrical infrastructure datasets, is employed to extract inventory features (e.g., pole types, insulators) and detect anomalies such as broken elements, corrosion, or vegetation encroachment (Zhang et al., 2021; Wang et al., 2020). The system is integrated into a GIS-backed web and mobile application, enabling real-time reporting and visualisation.

Results:
Field deployment across rural regions revealed that Powerline AI achieved over 92% mean Average Precision (mAP) in anomaly detection. Time spent on routine inspections decreased by 60% compared to manual methods, while early anomaly alerts enabled preemptive maintenance actions. In mountainous terrain, drone accessibility has significantly improved inspection coverage.

Conclusion:
This work demonstrates that AI-powered UAV inspection systems can enhance the accuracy, safety, and operational efficiency of powerline monitoring. Their integration with enterprise systems ensures daily usability, contributing to predictive maintenance frameworks and reducing long-term asset failure risks (Chen et al., 2020).