基于双层优化元学习的域自适应红枣缺陷检测

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中图分类号：S126 文献标识码：A 文章编号：2095-5553（2025）07-0104-07

Abstract：Toaddress the challenge posedbyvariations inreddate defectsacrossdiffrentvarietiesandlighting conditionsinautomatedsorting tasks，thisstudyproposedanovelmeta-learning-basedalgorithm fordomainadaptivedefect detection.First，across-domaindataset wasconstructedbycolecting images ofreddate defects frommultiplevarietiesand environmental conditions.To mitigatesampleimbalance，aditional defectsampleswere generatedusing the StyleGAN3 network，and data augmentation techniques were applied toenhance the diversity of testdataset.Next，abi-level optimizationmeta-learning framework was introduced fordomain-adaptivereddate defect detection.Aconvolutional neuralnetwork wasemployedasthebaselearner，whileadual-layeroptimizationstrategy wasusedtoconstruct themeta-learner.AnL2regularizationtermwasincorporatedintothelossfunctiontoreduce overfiting.Averageacuracywasused as theevaluationmetric.Ablation experiments wereconductedonboth the base learnerandthe meta-learner，and the proposed methodwas comparedagainst various deep learning and metalearningalgorithms to validate itsperformance.Experimental results demonstrated that theproposedmethod achieves average accuracies of 78.6% on the original target domain dataset and 86.5% on the augmented datasets， outperforming the MAML algorithm by 6.4% and 7.6% ，respectively. These findings confirm the method's effectiveness in adapting to cross-domain red date defect detection under diverse conditions.

Keywords： jujubes defect detection;domain adaptation;meta learning；bi-level optimization;L2 regularization

0 引言

红枣作为中华民族的代表性果品之一，口感甘甜，含有丰富的营养物质，被广泛应用于中医药和食品制作中。（剩余10443字）