A DINO remote sensing target detection algorithm combining efficient hybrid encoder and structural reparameterization

The DINO algorithm, tailored for remote sensing image detection, has garnered significant traction in recent research circles. Remote sensing object detection mandates algorithms to excel in both fine-grained feature extraction and large-scale spatial search capabilities. However, an improved DINO algorithm is proposed, the inherent multi-layer encoder-decoder architecture of the vanilla DINO algorithm incurs substantial spatial and computational overhead, posing notable impediments to real-time inference performance. To address these limitations, this study capitalizes on the inherent suitability of parallel large-kernel convolution structures for remote sensing image processing, proposing a single-layer efficient hybrid encoder architecture to enhance the parameter efficiency of the DINO algorithm framework. Within this novel encoder structure, we redesign the core module based on high-efficiency convolution operations and integrate structural parameterization techniques. This design strategically reduces both the number of trainable parameters and floating-point operations during inference, thereby effectively mitigating the latency bottleneck of the original DINO algorithm. Experimental evaluations on the NWPU VHR and DOTA datasets demonstrate that the improved DINO algorithm achieves marginal yet consistent performance gains, with mean average precision (mAP) improvements of 1.8% and 3.8% respectively compared to the baseline. Most notably, the proposed modifications yield substantial reductions in model size and computational complexity. When benchmarked against state-of-the-art remote sensing detection algorithms, the improved DINO algorithm maintains competitive detection accuracy while outperforming counterparts in terms of parameter efficiency, computational cost, and inference speed.