Food manufacturing requires reliable inspection systems to detect foreign material contamination and maintain product safety. Sub-THz transmission imaging provides material-dependent attenuation characteristics that are useful for detecting low-density contaminants in food products. However, existing unsupervised anomaly detection methods mainly rely on RGB-pretrained visual representations, which may not adequately capture the transmission behavior of Sub-THz images. This paper proposes a Beer-Lambert guided representation learning framework for unsupervised anomaly detection in Sub-THz food inspection images. The proposed method introduces an attenuation decomposition module as an auxiliary regularization module that constrains student representations through attenuation reconstruction during training. In addition to the conventional one-class setting, we introduce a Leave-One-Food-Out protocol to evaluate generalization capability under unseen food categories. Experimental results on the Inline-Food-Inspection-THz dataset show that the proposed method improves overall anomaly detection performance over the baseline method.

翻译：食品製造需要可靠的檢測系統來識別異物污染並確保產品安全。亞太赫茲透射成像能提供材料依賴性的衰減特性，有助於檢測食品中的低密度污染物。然而，現有的無監督異常檢測方法主要依賴於RGB預訓練的視覺表徵，可能無法充分捕捉亞太赫茲圖像的透射行為。本文提出一種基於比爾-朗伯定律的引導表徵學習框架，用於亞太赫茲食品檢測圖像的無監督異常檢測。該方法引入衰減分解模組作為輔助正則化單元，在訓練過程中透過衰減重建約束學生表徵。除傳統的單類設定外，我們提出留一食品類協議，以評估模型在未見過食品類別下的泛化能力。在Inline-Food-Inspection-THz資料集上的實驗結果表明，所提方法在整體異常檢測性能上優於基準方法。