Localize-diffusion based dual-branch anomaly detection

Due to the scarcity of real anomaly samples for use in anomaly detection studies, data augmentation methods are typically employed to generate pseudo anomaly samples to supplement the limited real samples. However, existing data augmentation methods often generate image patches with fixed shapes as anomalies in random regions. These anomalies are unrealistic and lack diversity, resulting in generated samples with limited practical value. To address this issue, we propose a dual-branch anomaly detection (DBA) technique based on Localize-Diffusion (LD) augmentation. LD can infer the approximate position and size of the object to be detected based on the samples’ color distribution: this can effectively avoid the problem of patch generation outside the target object’s location. LD subsequently incorporates hard augmentation and continuously propagates irregular patches to the surrounding area, which enriches the diversity of the generated samples. Based on the anomalies’ multi-scale characteristics, DBA adopts two branches for training and anomaly detection based on the generated pseudo anomaly samples: one focuses on identifying anomaly-specific features from learned anomalies, while the other discriminates between normal and anomaly samples based on residual features in the latent space. Finally, an adaptive scoring module is used to calculate a weighted average of the results of the two branches, achieving the goal of anomaly detection. Extensive experimental analyses reveal that DBA achieves excellent anomaly detection performance using only 14.2M parameters, notably achieving 99.6 detection AUC on the MVTec AD dataset.