YOLO-Based Detection and Classification of High-Voltage Insulator Surface Contamination

Outdoor high-voltage (HV) insulators are prone to surface contamination, increasing flashover risk and threatening power transmission systems reliability. Timely inspection is essential, but conventional visual inspections are costly, and inefficient for long transmission lines. An automated inspection using unmanned aerial vehicle images can be both efficient and low-cost. Given an appropriate training sample, a deep neural network such as YOLO (You Only Look Once) can be trained to localize insulators in diverse backgrounds and classify surface contamination. However, the wide variety of YOLO architectures proposed recently makes model selection challenging due to the accuracy-complexity trade-off. Rather than proposing a new model, this study focuses on selecting the “optimal” one. To this end: 1) a “laboratory” dataset of 15,000 insulator images with various surface contaminations is collected; 2) 21 YOLO architectures (YOLOv3–v11 and their variants) are trained, followed by a domain-specific (DS) model selection based on accuracy-complexity trade-offs; and 3) the selected DS-YOLOv11m and DS-YOLOv11n are fine-tuned on a small “industry” dataset of 356 images from a local HV substation to validate DS-specific pre-training in real-world scenarios. This work presents the first DS benchmarking of YOLOv3–v11 for insulator detection and contamination classification, resulting in a lightweight model optimized for real-time edge deployment. Evaluation of the fine-tuned models shows mAP@0.5 scores of 0.983 (DS-YOLOv11m) and 0.977 (DS-YOLOv11n). Although DS-YOLOv11n achieves slightly lower accuracy, it uses significantly fewer resources, reducing FLOPs by ~10.7 times. Finally, DS-YOLOv11n is deployed on a Raspberry Pi 5 to enable real-time contamination classification in edge-computing scenarios.