LogoCLIP:基于零样本学习的商标外观异常检测模型OA

As an important identifier of products,the visual quality of trademarks has a profound impact on consumers'shopping experiences as well as brand reputation.However,existing appearance defect detection methods in the industry primarily rely on training models with large amounts of data,making it difficult to apply them to the field of trademark appearance anomaly detection,which involves a wide variety of products,limited data,and non-fixed defect patterns.To address this issue,this paper proposes LogoCLIP:A zero-shot learning-based model for trademark appearance anomaly detection.Based on the vision-language pre-training model CLIP,LogoCLIP introduces learnable object-agnostic text prompt to guide the model to focus on anomalous regions rather than object semantics,enabling generalized anomaly detection across different types of objects.Then,a cross-modal feature fusion method based on attention mechanisms is proposed to achieve image-text feature alignment,and a gated fusion adapter is employed to address the distribution shift between pre-trained data and target data.By utilizing auxiliary datasets,the model achieves anomaly detection for trademark appearances without requiring training samples from trademark data.Finally,Experimental results on trademark appearance defect dataset demonstrate that LogoCLIP achieves image-level AUROC and pixel-level AUROC scores of 92.9%and 99.3%respectively.The model also achieved competitive detection results on three public industrial datasets—VisA,SDD and BTAD,which verified its strong generalization ability.