This work focuses on developing a smoke-detection model that can be used in an automated, end‑to‑end flare-smoke-detection, alerting, and distributed-control solution that leverages existing closed-circuit television (CCTV) cameras at manufacturing facilities. At the core of this solution is a deep-learning computer-vision model that leverages an extensive and diverse data set. The smoke-detection model uses a novel approach for real-time detection of hydrocarbon emission, using semantic segmentation through compact vision transformers (ViTs).
Introduction
The authors propose a novel application of efficient ViTs for predicting smoke regions in flare images. Because smoke detection is formulated as a semantic segmentation task, crisper boundaries and more-accurate predictions for smoke areas and densities are obtained.
