CSE: Surface Anomaly Detection with Contrastively Selected Embedding

TL;DR

This paper revisits approaches based on pre-trained features based on pre-trained features by introducing a novel method centered on target-specific embedding that derived a meaningful representation from the defect-free samples during training, facilitating a straightforward yet effective calculation of anomaly scores.

Abstract

Detecting surface anomalies of industrial materials poses a significant challenge within a myriad of industrial manufacturing processes. In recent times, various methodologies have emerged, capitalizing on the advantages of employing a network pre-trained on natural images for the extraction of representative features. Subsequently, these features are subjected to processing through a diverse range of techniques including memory banks, normalizing flow, and knowledge distillation, which have exhibited exceptional accuracy. This paper revisits approaches based on pre-trained features by introducing a novel method centered on target-specific embedding. To capture the most representative features of the texture under consideration, we employ a variant of a contrastive training procedure that incorporates both artificially generated defective samples and anomaly-free samples during training. Exploiting the intrinsic properties of surfaces, we derived a meaningful representation from the defect-free samples during training, facilitating a straightforward yet effective calculation of anomaly scores. The experiments conducted on the MVTEC AD and TILDA datasets demonstrate the competitiveness of our approach compared to state-of-the-art methods.

Figures (7)

• Figure 1: A comprehensive examination of the distinctions between our methodology and alternative embedding-based approaches during the inference phase. Limiting the comparison to a few specifically chosen samples, instead of encompassing the entire set of features, results in a considerable reduction in inference time.
• Figure 2: The complete training process. The training of the embedder constitutes the initial step, followed by the computation of clusters derived from the embedding representations.
• Figure 3: The defect generation process. N is the mask generated by thresholding a Perlin noise and (1-N) denote its negation. I is the original image.
• Figure 4: The decoder process for multi-layer embedder. Throughout the training process, both the pre-trained classifier and the decoder remain in a frozen state.
• Figure 5: An overview of MVTEC AD surfaces. The figure's upper section contains defect-free samples, whereas defective samples are situated in the lower part. Red encirclement highlights the defects.