Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry

TL;DR

This paper presents an active machine learning approach to improve quality assurance in virtual car renderings, reducing labeling effort while maintaining high defect detection accuracy in automotive visual inspections.

Contribution

It introduces a novel active learning system tailored for virtual car rendering quality assurance, significantly decreasing the need for labeled data compared to traditional methods.

Findings

Reduced labeling requirements for defect detection

Increased inspection efficiency at automotive manufacturer

Economic benefits through streamlined quality assurance

Abstract

Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with high-volume visual inspections due to the car models' increasing complexity. Even though the application of machine learning to many visual inspection tasks has demonstrated great success, its need for large labeled data sets remains a central barrier to using such systems in practice. In this paper, we propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings without compromising performance. By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized.