A lightweight insulator defect detection model for UAV inspection based on YOLOv11s

Abstract

Insulators play a vital role in ensuring the safe and stable operation of transmission lines. This study develops IDD-YOLO, an engineering-oriented lightweight detector for UAV-based insulator inspection, with emphasis on reducing model complexity while preserving the weak visual information of localized defects. GhostConv is used to reduce redundant computation in the backbone and neck, while a GhostConv–CARAFE lightweight neck combines efficient feature transformation with content-aware upsampling to preserve fine-grained defect information. EIoU is further employed as the bounding-box regression objective during training without adding inference-time network layers. On the IDID-Plus dataset, IDD-YOLO achieves a Precision of 83.9%, a Recall of 64.2%, and an mAP@0.5 of 66.3%, while requiring 4.2 M parameters and 9.6 GFLOPs. Compared with YOLOv11s, Precision, Recall, and mAP@0.5 increase by 4.3, 4.8, and 1.5 percentage points, respectively, whereas the parameter count and GFLOPs decrease by 54.3% and 41.5%. Although mAP@0.5:0.95 decreases slightly, the results demonstrate a competitive engineering-oriented trade-off between detection sensitivity and model complexity. The current study provides model-level evidence of lightweight design; practical deployment performance on UAV-compatible embedded hardware remains to be evaluated.