We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.
Cite
@article{arxiv.2202.01816,
title = {SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control},
author = {Joshua L. Pulsipher and Luke D. J. Coutinho and Tyler A. Soderstrom and Victor M. Zavala},
journal= {arXiv preprint arXiv:2202.01816},
year = {2022}
}