Deep Learning based Visual Quality Inspection for Industrial Assembly Line Production using Normalizing Flows

Abstract

The assembly line production of electrical consumer products is a highly streamlined process in which the product quality is continuously evaluated using automated checks. However, some products include manual processing due to customer requests that are not covered by standardized production plans. In such situations, quality issues frequently remain unnoticed leading to high reversal costs and customer dissatisfaction. We address this problem in a practical case study for a specific product family that is subject to highly versatile and error-prone configurations of externally exposed hardware connectors. In this setting, the worker must be visually assisted such that potentially faulty configurations are highlighted. Therefore, we investigate the applicability of state-of-the-art approaches for Anomaly Detection (AD) and Anomaly Localization (AL) on image data using pre-trained models and normalizing flows and compare against baseline Variational Auto-Encoders (VAEs). We show that those methods are not only applicable to well-established benchmarks on industrial image data but also have the potential to be used in a practical use case.