同时分类与异常检测，提升图像识别在未知场景下的鲁棒性。
Outliers resistant image classification by anomaly detection
- 用度量学习生成图像向量表示，融合分类与异常检测
- 在超32万张图像上验证，显著降低异常样本误判率
- 适合工业质检中应对未见物体的场景
各种技术，包括计算机视觉模型，被用于生产环境中人工装配过程的自动监控。这些模型可检测并分类组件是否出现在装配区或是否连接等事件。检测与分类算法的主要挑战在于对环境变化敏感，以及在处理训练数据集之外的物体时表现不可预测。由于无法将所有可能对象纳入训练样本，需另寻解决方案。本研究提出一种可同时进行分类与异常检测的模型，采用度量学习在多维空间中生成图像的向量表示，随后使用交叉熵进行分类。实验基于超过327,000张图像的数据集展开，测试了多种计算机视觉模型架构，并对比了各方法的性能表现。
原文摘要 · Abstract (English)
Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to variations in environmental conditions and unpredictable behavior when processing objects that are not included in the training dataset. As it is impractical to add all possible subjects in the training sample, an alternative solution is necessary. This study proposes a model that simultaneously performs classification and anomaly detection, employing metric learning to generate vector representations of images in a multidimensional space, followed by classification using cross-entropy. For experimentation, a dataset of over 327,000 images was prepared. Experiments were conducted with various computer vision model architectures, and the outcomes of each approach were compared.
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