Intelligent Emission Monitoring System Using Machine Vision Techniques

Abstract

Intelligent Emission Monitoring System Using Machine Vision Techniques

Skobelev K.D., Blagoveshchensky V.G.

Incoming article date: 12.04.2025

The article proposes an approach to creating an intelligent industrial emissions monitoring system based on the YOLO architecture and digital simulation. The work is relevant for improving the effectiveness of environmental control at industrial facilities, for example, an oil refinery. The system automatically detects and classifies smoke against a complex background (glare, fog, sky), combining real video data with synthetic images of a digital model of the site. Simulation settings and augmentation have been performed for different weather and light conditions. Experiments have shown that adding 30% synthetics to the training set increases classification accuracy, especially for subtle outliers. Recommendations on simulation parameters have been developed and the precision metric for pollution classes has been evaluated. The results confirm the effectiveness of the approach and its readiness to be implemented in automation.

Keywords: machine vision, digital simulation, emission monitoring, neural network models, pollution classification