MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization

With the rapid development of the digital manufacturing industry, the nature of quality data has transformed from simple univariate or multivariate characteristics to big data comprising multimedia elements such as images and videos. The utilization of image data for automated monitoring and anomaly detection has gained significant attention in recent years, which also poses new and complex challenges. A critical challenge is the substantial demand for sensing and computation resources. When these resources are limited, only a fraction of the image data can be observed and analyzed. Hence, adaptive sampling becomes imperative to select the most informative pixels that effectively capture anomaly information. In this article, we propose a novel recurrent neural network framework named Multi Task-Recurrent Attention Model (MT-RAM) which incorporates adaptive sampling for anomaly classification and localization in partially observable image data. MT-RAM emulates human-like perception by generating a sequence of glimpses to comprehend the image, with the location of each glimpse depending on the information gleaned from previous glimpses. Thorough numerical studies and case studies are conducted to evaluate the performance of MT-RAM in comparison to state-of-the-art adaptive-sampling-based anomaly detection methods.