RDT-YOLO: An Improved Lightweight Model for Fish Maw Authenticity Detection

With the rapid expansion of the global fish maw industry, the increasing prevalence of counterfeit products has made authenticity detection a critical challenge. Traditional detection methods rely on organoleptic assessment, chemical analysis, or molecular techniques, which limits their practical application. This paper treats fish maw authenticity detection as an object detection problem and proposes RDT-YOLO, a lightweight detection algorithm based on YOLO11n. Specifically, to address the challenges of insufficient fine texture feature extraction and computational redundancy in fish maw detection, we design hierarchical reparameterized feature extraction modules that utilize reparameterization technology to enhance texture feature extraction capability at different scales. To mitigate information loss during multi-scale feature fusion, we develop a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) module that incorporates dynamic convolution mechanisms for adaptive feature aggregation. Additionally, we propose an Adaptive Task-Aligned Detection Head (ATADH) that combines task interaction and shared convolution to reduce model parameters while improving detection accuracy. Furthermore, a Wise-ShapeIoU loss function is introduced by incorporating a focusing coefficient into Shape-IoU, enhancing model detection performance through improved bounding box shape optimization. Experimental validation demonstrates that RDT-YOLO achieves 91.9% precision, 89.6% recall, and 94% mAP@0.5 while reducing parameters, model size, and computational complexity by 75.6%, 73.8%, and 63.8%, respectively, compared to YOLO11s. When evaluated against YOLOv10s and YOLOv12s, RDT-YOLO shows mAP@0.5 improvements of 0.8% and 0.5%, respectively. This work provides an automated solution for fish maw authenticity detection with potential for broader food safety applications.