IDD-DETR: Lightweight Multi-Defect Detection Model for Transmission Line Insulators Based on UAV Remote Sensing

Aiming to address the challenges of excessive model parameters, high computational complexity, strong complex background interference, and inadequate small-target detection found in insulator defect detection when using UAV remote sensing imagery of transmission lines, we propose a lightweight multi-defect detection model—Insulator Defect Detection-DETR (IDD-DETR). Specifically, we introduce a lightweight multi-starblock feature extractor (LMS-FE) as the backbone network to enhance its feature extraction capacity. Next, in order to enhance small-defect detection performance, a multi-scale feature pyramid (SOEP) is constructed by integrating shallow high-resolution features into the neck network. Additionally, a lightweight multi-branch feature fusion module (LMB-FF) is designed to efficiently fuse spatial and semantic information of small defects, suppressing background interference while optimizing model complexity. Finally, experimental results demonstrate that IDD-DETR achieves a 2.2% improvement in mean average precision (mAP) on the insulator small-defect dataset compared with the baseline algorithm, with model parameters and computation reduced by 44.9% and 47.1%, respectively. It also reaches a detection speed of 61.2 frames per second, satisfying the lightweight and high-precision requirements for edge deployment in transmission line inspection scenarios.