用多视角变形DETR自动评估大型家电翻新品的视觉质量
Visual Quality Score Assessment of Large White Goods in Remanufacture with Multi-View Deformable-DETR
- 基于多视角变形DETR融合冗余视图信息，提取细粒度特征
- 在有限标注下实现高精度质量评分，工业数据集上表现优异
- 支持可解释性定位缺陷区域，适合制造业质检场景
翻新大型白色家电对循环经济至关重要，但视觉质量评估仍依赖人工，成为培训与定价的瓶颈。传统检测方法需大量标注，且在高分辨率多视角数据中难以识别微小缺陷。我们提出一种基于Deformable-DETR的多视角框架，通过跨视图信息聚合提取细粒度特征。为增强少样本下的鲁棒性，采用自监督预训练结合专家标注评分的有监督微调。此外，对冻结特征图进行线性投影，定位关键关注区域以解释模型决策。在工业级多视角数据集上验证，该方法显著降低对人工标注和单部件定制的依赖，实现可扩展、透明的质量检测，适用于翻新产线。
原文摘要 · Abstract (English)
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.
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