Work

Detecting visual anomalies in an industrial environment: Unsupervised methods put to the test on the AutoVI dataset

Year: 2024

Type: article

Abstract: The methods for unsupervised visual inspection use algorithms that are developed, trained and evaluated on publicly available datasets. However, these datasets do not reflect genuine industrial conditions, and thus current methods are not evaluated in real-world industrial production contexts. To answer this shortcoming, we introduce AutoVI, an industrial dataset of visual defects that can be encountered on automotive assembly lines. This dataset, comprising six inspection tasks, was designed as a benchmark to assess the performance of defect detection methods under realistic acquisition conditions. We analyze the performance of current state-of-the-art methods and discuss the difficulties specifically encountered in the industrial context. Our results show that current methods leave considerable room for improvement. We make AutoVI publicly available to develop unsupervised detection methods that will be better suited to real industrial tasks.

Source: Computers in Industry

DOI: 10.1016/j.compind.2024.104151

Authors: Philippe Carvalho, Meriem Lafou, Alexandre Durupt, Antoine Leblanc, Yves Grandvalet

Institutions: Université de Technologie de Compiègne, Laboratoire Roberval, Renault (France), Centre National de la Recherche Scientifique

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,170