FusionLite-DETR: A Lightweight Transformer-Based Model for UAV-Based Insulator Defect Detection

Aiming to overcome the challenges associated with the unmanned aerial vehicle (UAV)-based inspection of transmission line insulators, including the tiny scale of defect regions, irregular morphologies, and low contrast with complex backgrounds, we propose a lightweight insulator defect detection model based on real-time detection transformer (RT-DETR), termed FusionLite-DETR. To improve detection accuracy while maintaining computational efficiency, the proposed model introduces systematic enhancements to RT-DETR in four aspects: the backbone network, feature reconstruction operator, feature fusion module, and loss function. Firstly, the backbone network is reconstructed by integrating deformable large kernel attention (DLKA) into VanillaNet, enabling the model to couple global perception with precise local focusing, thereby significantly enhancing feature extraction for multi-scale defects. Secondly, the defect-aware content-aware reassembly of features (DA-CARAFE), is proposed to replace the conventional upsampling method. It dynamically generates reassembly convolution kernels based on underlying spatial features, achieving semantically aligned feature fusion under lightweight constraints. Meanwhile, a wavelet transform-based C3 (WTC3), is designed. By performing feature representation and interaction across different frequency sub-bands and incorporating ghost-shuffle convolution (GSConv), the module effectively reduces parameter redundancy while preserving critical high-frequency texture information. Furthermore, by introducing the inner minimum point distance intersection over union (Inner-MPDIoU) loss function, the boundary discrepancies are accurately captured by combining auxiliary-box overlap measurement with vertex-based geometric distance constraints, thus effectively mitigating the gradient vanishing problem for distant small targets during regression. FusionLite-DETR achieved mean average precision at an intersection-over-union threshold of 0.50 (mAP50) values of 95.5% and 93.1% on the private multi-scenario insulator defect dataset (PMID) and insulator fault detection dataset IFDD (datasets), respectively, with 19.1 M parameters and a computational cost of 54.8 giga floating-point operations (GFLOPs). Compared with RT-DETR with a resnet-50 backbone (RT-DETR-R50), the proposed model reduced the parameter count and GFLOPs by 54.8% and 57.4%, respectively, demonstrating a favorable balance between detection performance and model complexity.