MG-fusion-DETR: a lightweight transformer-based multi-granularity detection model

Abstract Against the backdrop of accelerating intelligent transformation in global energy infrastructure, establishing a secure, stable, and efficient smart monitoring system for transmission and distribution grids has become a core requirement. In response to the increasingly severe threat of foreign object intrusion in transmission lines, traditional reliance on manual inspections struggles to meet the demands of real-time proactive defense due to inherent limitations such as blind spots in mountainous areas, difficulty in identifying small targets, and delayed fault response. To overcome these challenges, this study proposes a lightweight object detection model based on the Transformer architecture—Multi-Granularity Fusion-DETR (MG-Fusion-DETR)—aiming to achieve a delicate balance between detection performance and computational efficiency. The core innovation of the model lies in two aspects: firstly, a Multi-Granularity Fusion Attention (MGFA) mechanism is proposed, which achieves precise focusing and discriminative learning for complex texture features and small-scale foreign objects by coupling pooled channel attention with an original multi-scale depthwise separable convolution structure; secondly, a bottleneck residual unit based on exponential moving average (EMA) fast convolution is designed, which significantly reduces computational overhead while preserving fine-grained feature extraction capability, laying the foundation for lightweight deployment of the model. On this basis, we construct a Multi-Granularity Context Fusion Transformer Encoder to integrate the global-local modeling advantages of MGFA, and introduce an auxiliary global context coordination module to further optimize feature fusion. Experimental results demonstrate that MG-Fusion-DETR achieves detection accuracies of 85.3% and 87.3% on public and self-built high-definition foreign object detection datasets, respectively, while reaching an inference speed of 160 FPS. This fully validates its outstanding performance and significant engineering potential for real-time foreign object detection tasks on power lines.