A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing

TL;DR

This paper introduces a new dataset and a contrastive regularization method to improve deep learning models for visual quality inspection in remanufacturing, focusing on generalization to unseen components and defects.

Contribution

The paper presents a novel dataset for gearbox component inspection and proposes a contrastive regularization loss to enhance model robustness and generalization.

Findings

Contrastive regularization improves generalization to unseen components.

Models trained with the proposed loss perform better on distribution shifts.

The dataset enables benchmarking of model robustness in remanufacturing inspection.

Abstract

Remanufacturing describes a process where worn products are restored to like-new condition and it offers vast ecological and economic potentials. A key step is the quality inspection of disassembled components, which is mostly done manually due to the high variety of parts and defect patterns. Deep neural networks show great potential to automate such visual inspection tasks but struggle to generalize to new product variants, components, or defect patterns. To tackle this challenge, we propose a novel image dataset depicting typical gearbox components in good and defective condition from two automotive transmissions. Depending on the train-test split of the data, different distribution shifts are generated to benchmark the generalization ability of a classification model. We evaluate different models using the dataset and propose a contrastive regularization loss to enhance model robustness. The results obtained demonstrate the ability of the loss to improve generalisation to unseen types of components.