  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.
