Remanufacturing large white goods is essential for a circular economy, yet visual quality assessment remains a manual bottleneck for training and pricing. Conventional detection methods require extensive annotation and struggle with small defects in high-resolution multi-view data. We present a multi-view framework based on Deformable-DETR for automated quality scoring that aggregates information across redundant views to extract fine-grained features. To enhance robustness with limited labels, we employ self-supervised pretraining followed by supervised fine-tuning on expert-annotated scores. Additionally, a linear projection over frozen feature maps identifies regions of interest to explain model decisions. Evaluated on an industrial multi-view dataset, our approach delivers precise quality assessments while reducing reliance on manual annotation and per-part customization, enabling scalable and transparent inspection for remanufacturing lines.

翻译：大型白色家电的再制造是循环经济的重要环节，然而视觉质量评估仍是制约其培训与定价的人工瓶颈。传统检测方法需要大量标注，且在针对高分辨率多视角数据中的微小缺陷时表现不佳。我们提出了一种基于Deformable-DETR的多视角框架，用于自动质量评分，该框架通过聚合冗余视角的信息来提取细粒度特征。为增强在有限标注条件下的鲁棒性，我们采用自监督预训练结合专家标注评分的监督微调策略。此外，在冻结特征图上进行线性投影可识别感兴趣区域，从而解释模型决策。在工业多视角数据集上的评估表明，我们的方法既能实现精确的质量评估，又能减少对人工标注和单部件定制的依赖，为再制造产线提供了可扩展且透明的检测方案。