构建齿轮箱部件质检数据集，提升模型对新零件的泛化能力。
A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing
- 构建双车型齿轮箱部件图像数据集，涵盖正常与缺陷样本。
- 提出对比正则化损失，使模型在未见零件上准确率提升12.3%。
- 适合工业质检、迁移学习研究者参考。
再制造是将磨损产品恢复至类新状态的过程，具有显著的生态与经济潜力。关键步骤是拆解部件的质量检测，因零件类型和缺陷模式多样，目前主要依赖人工。深度神经网络虽有自动化潜力，但在新产品变体、部件或缺陷模式下泛化能力差。为此，我们提出一个新图像数据集，包含两个汽车变速箱中典型齿轮箱部件在正常与缺陷状态下的图像。根据训练-测试划分方式，生成不同分布偏移，用于评估分类模型的泛化能力。我们在该数据集上评估多种模型，并提出一种对比正则化损失以增强模型鲁棒性。实验结果表明，该损失能有效提升模型对未见部件类型的泛化能力。
原文摘要 · Abstract (English)
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.
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