MSO-DETR: A Lightweight Detection Transformer Model for Small Object Detection in Maritime Search and Rescue

In maritime search and rescue small object detection, existing high-accuracy detection models face deployment challenges on UAV platforms due to limited computational capabilities, while existing lightweight models often fail to meet performance requirements, reducing the overall effectiveness of rescue operations. To overcome the difficulty of balancing lightweight design and detection accuracy, we propose Maritime Small Object Detection Transformer (MSO-DETR), a lightweight detection transformer model for small object detection in maritime search and rescue, based on an improved Real-Time Detection Transformer (RT-DETR) architecture. MSO-DETR employs StarNet as its backbone to reduce the computational cost with a slight drop in detection accuracy. In addition, the Dynamic-range Histogram Self-Attention (DHSA) mechanism is integrated with the Attention-based Intra-scale Feature Interaction (AIFI) module to construct DHAIFI, which enhances the model’s ability to perceive object features under challenging conditions such as sea surface reflections and wave interference. During the feature fusion phase, we propose the Scale-Tuned Enhanced Feature Fusion (STEFF) module, which integrates the improved Attentional Scale Sequence Fusion (ASF) structure with the newly designed Multi-Dilated Convolution Cross-Stage Partial (MDC_CSP) and Parallel Aggregation Downsampling (PAD) to enhance multi-scale aggregation and small object recognition while maintaining computational efficiency. Experimental results demonstrate that, in contrast to the baseline, MSO-DETR achieves significant model lightweighting, reducing parameters by 67.3% and GFLOPs by 46.5%, while maintaining detection accuracy on the SeaDronesSee dataset, with only a 0.1% decrease in mAP50 and a 0.5% improvement in mAP50:95. It also delivers comparable performance to the baseline on the AFO dataset.