An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3

With the advent of smart distribution grids, detection of defects in insulators with unmanned aerial vehicles as a part of distribution automation system (DAS) has attained a widespread attention. The defects are essential to detect to avoid damaging the service life of distribution lines, serious power loss and cascading power outages in extreme conditions. The intricate background, limited image dataset and small-scale object makes the problem of detection more complex. Owing to the exponential advancement in deep learning, deep learning-based insulator defect detection is gradually attaining a foothold in the research domain. This paper presents a novel approach for detecting insulator defects in an 11 kV distribution system using a modified version of You Only Look Once (YOLO V11) and the MobileNetV3 model. Data augmentation was applied as part of the preprocessing phase to train the proposed model. The model's performance was compared with earlier versions of YOLO and other existing methods to demonstrate its effectiveness. Additionally, multiple case studies were conducted to validate the method's robustness and reliability for insulator defect detection. This paper incorporates a modified version of YOLOv11 architecture using the constituent C3K2, SPFF and C2PSA algorithmic blocks, mounted with a MobileNetV3 classifier to allow lightweight framework in DAS based devices. Studies involving various real-life scenarios show the efficacy and applicability of the proposed algorithmic pipeline.