食品与机械2025，Vol.41Issue(10)：52-58,7.DOI:10.13652/j.spjx.1003.5788.2024.81203
基于小样本学习与特征配准的面饼异常检测
Instant noodle anomaly detection based on few-shot learning and feature alignment
摘要
Abstract
[Objective]To achieve efficient detection of fine foreign objects in instant noodle,this paper proposes an instant noodle anomaly detection method based on few-shot learning and feature alignment.[Methods]The method uses a pre-trained residual network as a feature extraction network for efficiently extracting the features of the instant noodle image.The image is geometrically transformed by introducing a spatial transformation network for better alignment and extraction of features.Feature alignment is used to align the key features in the image so that the accuracy and generalization ability of the model can be ensured in detecting instant noodle anomalies.[Results]Experiments are conducted on a self-made instant noodle dataset,and the proposed method achieves areas under the curve(AUCs)of 86.2%and 93.3%at the 5-shot image level and pixel level,respectively,which outperforms other methods on the instant noodle dataset.[Conclusion]The proposed method can effectively detect fine foreign objects in the instant noodle anomaly detection task.关键词
面饼检测/小样本学习/孪生网络/异常检测/残差网络Key words
instant noodle detection/few-shot learning/Siamese network/anomaly detection/residual network引用本文复制引用
马凯龙,杨超宇..基于小样本学习与特征配准的面饼异常检测[J].食品与机械,2025,41(10):52-58,7.基金项目
国家自然科学基金资助项目(编号:52227901) （编号:52227901）
