  In this study, state-of-the-art unsupervised detection models were evaluated
for the purpose of automated anomaly inspection of wool carpets. A custom
dataset of four unique types of carpet textures was created to thoroughly test
the models and their robustness in detecting subtle anomalies in complex
textures. Due to the requirements of an inline inspection system in a
manufacturing use case, the metrics of importance in this study were accuracy
in detecting anomalous areas, the number of false detections, and the inference
times of each model for real-time performance. Of the evaluated models, the
student-teacher network based methods were found on average to yield the
highest detection accuracy and lowest false detection rates. When trained on a
multi-class dataset the models were found to yield comparable if not better
results than single-class training. Finally, in terms of detection speed, with
exception to the generative model, all other evaluated models were found to
have comparable inference times on a GPU, with an average of 0.16s per image.
On a CPU, most of these models typically produced results between 1.5 to 2
times the respective GPU inference times.
