LFODet: Lightweight Few-Shot Object Detection with Meta-Learning in Remote Sensing Images

Balancing detection accuracy with model lightweightness remains a key challenge in remote sensing object detection. Although convolutional neural networks have improved performance, they typically require large-scale datasets, making few-shot detection of novel classes difficult. To tackle this, we propose LFODet, a lightweight few-shot object detection network based on meta-learning. It uses two parallel branches to rapidly adapt to novel classes with limited samples while maintaining performance on base classes. For efficient feature representation, we integrate Semantic Ghost Channel Attention (GCA) and Fine-Grained Ghost Spatial Attention (GSA) to enhance semantic discriminability and spatial detail preservation. Moreover, we leverage Ghost convolutions to reduce computational complexity. The model is trained in three stages: base-class pre-training, meta-learner optimization, and few-shot fine-tuning. Experiments on DIOR and NWPU VHR-10 demonstrate that LFODet achieves stable and balanced performance across various few-shot learning scenarios. As validated on these benchmark datasets, this work provides a practical solution for resource-constrained remote sensing applications requiring rapid adaptation to new targets.