Vision-Based Monitoring of Flare Soot
Ke Gu, Yonghui Zhang, Junfei Qiao
Abstract
The flare stack is a typical flare gas combustion facility used to guarantee the safe production of petrochemical plants, refineries, and other enterprises. One of the most vital problems of a flare stack is the incomplete combustion of flare gas, which produces a large amount of flare soot and, thus, endangers the atmosphere and human health. Hence, an effective and efficient flare soot monitoring system that has important guiding significance to environmental protection is strongly required. To this end, we devise a vision-based monitor of flare soot (VMFS) that can search for flare soot in a timely way and ensure the full combustion of flare gas. First, the proposed VMFS leverages the broadly tuned color channel to recognize a flame in an input video frame since the flame is the source of flare soot in our application. Second, our monitor incorporates fast saliency detection with K-means to fix the position of the flame. Third, we take the flame area as the center to search for the potential flare soot region, followed by identifying the flare soot based on the background color channel. The results of experiments on multiple video sequences collected at a real petrochemical plant reveal that the proposed VMFS is superior to state-of-the-art relevant models in both monitoring performance and computational efficiency. The implementation code will soon be released at https://kegu.netlify.com/.