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.

翻译：包括计算机视觉模型在内的多种技术被应用于生产过程中手动装配流程的自动监控。这些模型能够检测并分类诸如装配区域存在组件或组件连接等事件。检测与分类算法面临的主要挑战在于其对环境条件变化的敏感性，以及在处理训练数据集中未包含的对象时可能出现的不可预测行为。由于在训练样本中添加所有可能对象并不现实，因此需要替代解决方案。本研究提出了一种同时执行分类与异常检测的模型，该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。实验准备了包含超过327,000张图像的数据集，对多种计算机视觉模型架构进行了测试，并对各方法的结果进行了比较分析。