3 accepted papers
Obtaining labeled data in the field of industrial anomaly detection is challenging, which necessitates the development of label-free frameworks. However, current methods mainly focus on the unsupervised paradigm, which uses a large number of normal samples of the same category to train the model, an…
Industrial anomaly detection (IAD) plays a crucial role in large-scale industrial manufacturing. Recently, numerous unsupervised algorithms have been proposed and achieved remarkable performance on benchmark datasets. Given the high homogeneity of samples during training and testing, it appears that…
In this paper, a vision-based method is proposed for cable installation tasks in constrained environments. The main challenge of such tasks lies in the potential interference between the cable and surrounding obstacles. Model-based approaches are not well-suited for these industrial scenarios due to