Real-Time Condition Monitoring of Transmission Line Insulators Using the YOLO Object Detection Model With a UAV

Continuous monitoring and inspection of high-voltage insulators are necessary to prevent failures and emergencies. Manual inspections can be costly and time-consuming, particularly when covering large geographical areas exposed to harsh weather conditions. This study proposed a single-stage object detector approach to address the limitations of traditional inspection methods by utilizing a hexacopter for efficient inspections of outdoor insulators. The object detector model was trained using a dataset of 6020 insulator images for detecting defects in complex backgrounds. Image augmentation techniques were adopted to avoid overfitting. Finally, the hexacopter was equipped with an onboard camera and a Raspberry Pi 4 single-board computer to automate the outdoor insulator inspection system by detecting real-time defects. Experimental results demonstrated the effectiveness of the YOLOv8n object detector model in successfully identifying various insulator conditions, including normal, broken, polluted, and flashover surfaces, with a mAP@50 of 99.4%.