Partial Camera Obstruction Detection Using Single Value Image Metrics and Data Augmentation

Abstract

To improve the resilience and ensure the dependability of a critical system, the measurements and the derived intelligence provided by the sensors monitoring the system need to be reliable. This is increasingly challenging. As the computer vision methods evolve, the usage of cameras as a part of monitoring solutions has increased, and, consequently, the need for reliable diagnosis strategies for those image-based sensors. This work investigates the suitability of various single-value image metrics, derived from first and second-order statistics, for detecting partial camera obstruction. The presented methodology includes using data augmentation techniques to expand a small dataset of labeled images, and a score-based selection of the best metrics for the target application. The results show that even simple first-order statistics, such as the image histogram skewness, can provide good detection results. The strategy presented could be extended and adapted for the detection of other types of physical anomalies, being particularly useful for integrity assessment in applications with limited computational resources.

Data Augmentation of Sensor Time Series using Time-varying Autoregressive Processes

“…In particular, data augmentation methods have improved the performance of diagnostics approaches with limited data in different PHM use cases (Yang et al, 2023;D. Wang, Dong, Wang, & Tang, 2023;Li, Zhang, & Zhang, 2023;Jiang & Ge, 2021;Shen, Yao, Jiang, Yang, & Zeng, 2023;de Oliveira, Niemi, García-Ortiz, & Torres, 2023;Taghiyarrenani & Berenji, 2022). This work proposes a novel method for augmentation of multivariate time-series data based on time-varying autoregressive (TVAR) models.…”