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Water Bath Scallop Shucking System Based on Doneness Detection
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AI
DOI：10.3390/s26144545.png)
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Abstract
En 中文
In the scallop shucking industry, labor-intensive manual operations and inconsistent product quality remain persistent challenges. To address these issues, this study develops a scallop shucking system based on visual feedback temperature control. To support this system, systematic experiments are conducted to determine the baseline shucking method and its initial parameters. On this basis, the scallop doneness detection model SDD-RT-DETR, serving as the system’s core, integrates an enhanced backbone centered on the self-developed module HierarchicalRepBlock, a frequency-domain self-attention module AIFI-EDFFN, a neck featuring the self-developed EfficientBalanceFusion module as the feature fusion unit and the Converse2DC3 module as the feature extraction unit, and the Wise-DIoU loss function. A scallop doneness image dataset specifically constructed for this task was used to train and validate this model. Experimental results demonstrate that the model achieves 95.5% accuracy, 93.6% recall, and 96.1% mAP50, improving by 4.7%, 3.2%, and 4.2%, respectively, over the baseline model RT-DETR. Additionally, the model’s computational cost was reduced by 18.9%, and its parameters were reduced by 14.1% compared to the baseline model. This model provides real-time, accurate doneness assessment, thereby filling a gap in computer vision for scallop doneness detection. Furthermore, this study integrated this model into a water bath scallop shucking system with feedback temperature control, ensuring that the doneness of scallops after shucking is maintained within an optimal range. Batch tests show that this system achieves a 96.6% shucking rate and an 88.5% properly cooked rate, outperforming the conventional method. This improves the quality of automatically shucked scallops while offering a practical solution for the intelligent upgrading of the seafood processing industry.
Keywords:
scallop shucking
visual feedback
doneness detection
RT-DETR
temperature control
deep learning