船载泵后通道鱼体实时检测:基于MGI-RTDETR的端到端方法OA

This study targets detection degradation in the pump-downstream channel(conveyor/dewatering chute)of light-luring purse-seine operations in the South China Sea,where fish exhibit small size,high speed,strong motion blur,and dense occlusion.A blur-tolerant,detail-preserving real-time detector,MGI-RTDETR,is presented.Built on RT-DETR,it integrates Multi-Scale Grouped Interaction features(MGI-FE),Adaptive Dynamic Context Mixing(ADC-MI),Adaptive Detail-Preserving Fusion(ADPF),deployment-time Consolidated Re-parameterization(CRC),and an Adaptive Reverse-Projection Upsampler(ARPU),improving small-object boundary restoration and separability in crowded scenes without increasing inference-time complexity.A 396-image single-class dataset(train/val/test=7/1/2)is used under a unified 640×640 training/inference protocol.On the test set,the method attains Precision/Recall/F-Score of 88.54%/75.03%/81.23%,mAP50 of 81.00%,and mAP50-95 of 28.29%,while maintaining deployment-friendly cost(44.5 GFLOPs,13.39 M parameters,～50.46 FPS),outperforming RT-DETR-R18 and multiple YOLO/SSD baselines.To illustrate downstream usability,ByteTrack is stacked without modifying the detector to realize line-crossing counting;counting precision(CP)at low/medium/high densities is 95.0%/80.8%/49.7%.To assess cross-vessel generalization,an external test set of 309 images from three additional vessels is evaluated,confirming trends consistent with the main experiment under varied viewpoints and illumination.The results indicate that multi-scale interaction and cross-scale adaptive fusion oriented to blur and fine-detail preservation provide a practical foundation for near-real-time,georeferenced catch analytics on working vessels,supporting transparency and data-driven fisheries management.