用视觉特征实时监测火炬燃烧质量，无需传感器。
Flame quality monitoring of flare stack based on deep visual features
- 通过图像分割、目标检测等提取火焰与烟雾面积比、颜色等视觉特征。
- 结合主成分分析与GPT-4实现燃烧效率识别，支持实时预警。
- 适合石油石化行业用于环保合规与节能优化。
火炬在石油化石能源厂中用于处理废气和废物，其燃烧效率监测对环境保护至关重要。传统传感器监测方式成本高，且易在恶劣燃烧环境中损坏。本文提出仅基于视觉特征的火焰质量监测方法，包括火焰与烟雾面积比、火焰RGB信息、火焰角度等特征。综合运用图像分割、目标检测、目标跟踪、主成分分析及GPT-4等技术完成任务。最终实现视频流的实时监测，当燃烧效率低下时可及时调整空气与废料比例。据我们所知，该方法具有较强创新性，具备工业应用价值。
原文摘要 · Abstract (English)
Flare stacks play an important role in the treatment of waste gas and waste materials in petroleum fossil energy plants. Monitoring the efficiency of flame combustion is of great significance for environmental protection. The traditional method of monitoring with sensors is not only expensive, but also easily damaged in harsh combustion environments. In this paper, we propose to monitor the quality of flames using only visual features, including the area ratio of flame to smoke, RGB information of flames, angle of flames and other features. Comprehensive use of image segmentation, target detection, target tracking, principal component analysis, GPT-4 and other methods or tools to complete this task. In the end, real-time monitoring of the picture can be achieved, and when the combustion efficiency is low, measures such as adjusting the ratio of air and waste can be taken in time. As far as we know, the method of this paper is relatively innovative and has industrial production value.
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