Outliers resistant image classification by anomaly detection
This addresses the challenge of robust image classification in production assembly for industries, but it appears incremental as it combines existing techniques like metric learning and anomaly detection.
The study tackled the problem of computer vision models being susceptible to environmental variations and unpredictable behavior with unseen objects in manual assembly monitoring by proposing a model that simultaneously performs classification and anomaly detection using metric learning and cross-entropy, achieving results on a dataset of over 327,000 images.
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.