Emission management

Deep-Learning-Based Flare-Smoke Detection Allows Real-Time Application

This paper focuses on developing a model that can be used in an automated, end-to-end flare-smoke detection, alert, and distribution-control solution that leverages existing flare closed-circuit television cameras at manufacturing facilities.

Fig. 1—Model predictions under various lighting and background sky conditions.
Fig. 1—Model predictions under various lighting and background sky conditions.
Source: SPE 220857.

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.

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