从基准测试到部署：面向工业纺织录入的移位鲁棒织物识别

中文摘要 AI 辅助

自动识别织物的组织结构（平纹、斜纹、缎纹）是纺织采购中的一个瓶颈，因为新到的样品仍需人工分类。基准测试的准确率表明该问题已解决，但实际部署中却难以奏效。在包含\ umClasses{}个类别的FabricFlow基准上，我们揭示了三个被总体准确率掩盖的差距。首先，重复性审计发现训练/测试数据泄漏会虚增准确率；我们重建了无泄漏的数据划分，以反映真实难度。其次，在干净数据上，导致识别失败的主要原因是目录间的采集源偏移，而非人们可能担心的边缘捷径：在仅含档案的留出集上，标准训练的Top-1准确率为58.0%，校准误差为0.158，而一种简单、与架构无关的中心纹理方法额外提升了13.5个Top-1百分点，并恢复了校准。第三，由于混淆不同织物家族的代价高于家族内的误判，我们优化了一个分类学严重性成本：一种置信度门控的路由策略自动分类高置信度样品，仅将不确定的少数样品转交人工处理，从而大幅降低录入成本。在整个过程中，我们报告了诚实的负面结果：层次分类、OCR融合和零样本视觉-语言模型均未能提供帮助，最终形成了一套具体、校准且成本感知的可部署纺织录入方案。

英文摘要

Automatically recognising a fabric's construction (jersey, twill, satin) is a bottleneck in textile sourcing, where incoming swatches are still typed by hand. Benchmark accuracy suggests the problem is solved, yet rarely survives deployment. On the \numClasses{}-class FabricFlow benchmark we expose three gaps that headline accuracy hides. First, a duplication audit reveals train/test leakage that inflates accuracy; we rebuild leakage-free splits that report the true difficulty. Second, on the clean data the binding failure is acquisition-source shift between catalogues, not the peripheral shortcuts one might fear: on an archive-exclusive hold-out, standard training holds 58.0\% Top-1 at a calibration error of 0.158, while a simple, architecture-agnostic central-texture recipe adds 13.5 Top-1 points and restores calibration. Third, because confusing one fabric family for another is costlier than a within-family slip, we optimise a taxonomic-severity cost: a confidence-gated routing policy auto-types confident swatches and refers only the uncertain minority to a human, sharply cutting onboarding cost. Throughout we report honest negatives: hierarchical classification, OCR fusion and zero-shot vision--language models all fail to help, yielding a concrete, calibrated, cost-aware recipe for deployable textile onboarding.

发表机构

• Laboratory for Artificial Intelligence in Design（设计人工智能实验室）
• School of Design, Royal College of Art（皇家艺术学院设计学院）
• Dept. of Industrial and Systems Engineering, The Hong Kong Polytechnic University（香港理工大学工业及系统工程学系）

机构由 AI 辅助整理，请以论文原文为准。