ISSN :2582-9793
Soldatov Aleksei Valerievich, Anton Valerievich Sergeev, Victor Olegovich Minchenkov, Yaroslav Andreevich Mazikov and Vasiliy Vladimirovich Kakurin
Adv. Artif. Intell. Mach. Learn., 5 (1):3344-3355
1. Soldatov Aleksei Valerievich: National Research University Higher School of Economics
2. Anton Valerievich Sergeev: National Research University Higher School of Economics
3. Victor Olegovich Minchenkov: National Research University Higher School of Economics
4. Yaroslav Andreevich Mazikov: National Research University Higher School of Economics
5. Vasiliy Vladimirovich Kakurin: National Research University Higher School of Economics
DOI: https://dx.doi.org/10.54364/AAIML.2025.51191
Article History: Received on: 07-Dec-24, Accepted on: 06-Mar-25, Published on: 13-Mar-25
Corresponding Author: Soldatov Aleksei Valerievich
Email: Soldatovalex34@gmail.com
Citation: Anton Sergeev, Victor Minchenkov, Soldatov Aleksei Valerievich, Vasiliy Kakurin, Yaroslav Mazikov. (2025). Outliers resistant image classification by anomaly detection. Adv. Artif. Intell. Mach. Learn., 5 (1 ):3344-3355
The automatic monitoring of manual assembly processes in production settings increasingly relies on advanced technologies, including computer vision models. These models are designed to detect and classify events such as the presence of components in an assembly area and the connection of these components. However, a significant challenge for detection and classification algorithms is their vulnerability to variations in environmental conditions and their unpredictable behavior when encountering objects that are not present in the training dataset. Due to the impracticality of including all potential objects into the training sample, alternative solutions are needed. This study introduces a model that combines classification with anomaly detection by leveraging metric learning to create vector representations of images in a multidimensional space, followed by classification using cross-entropy. A dataset of over 327,000 images has been prepared for this purpose. Comprehensive experiments were conducted using various computer vision models, and the performance of each approach was systematically evaluated and compared.