AURA: A Hybrid Spatiotemporal-Chromatic Framework for Robust, Real-Time Detection of Industrial Smoke Emissions

TL;DR

AURA is a hybrid framework that combines spatiotemporal and chromatic analysis to detect and classify industrial smoke emissions accurately in real-time, addressing limitations of existing systems.

Contribution

The paper presents a novel hybrid approach integrating movement patterns and color features for improved industrial smoke detection and classification.

Findings

Enhanced detection accuracy over existing methods

Reduced false positive rates in smoke monitoring

Effective real-time performance in industrial environments

Abstract

This paper introduces AURA, a novel hybrid spatiotemporal-chromatic framework designed for robust, real-time detection and classification of industrial smoke emissions. The framework addresses critical limitations of current monitoring systems, which often lack the specificity to distinguish smoke types and struggle with environmental variability. AURA leverages both the dynamic movement patterns and the distinct color characteristics of industrial smoke to provide enhanced accuracy and reduced false positives. This framework aims to significantly improve environmental compliance, operational safety, and public health outcomes by enabling precise, automated monitoring of industrial emissions.