Abstract
Accurate emission rate data are essential for assessing the environmental impact of industrial emissions and calibrating the effectiveness of desulphurization equipment. However, accurate calculation of SO2 emissions is difficult to realize due to the environmental conditions, the time-varying and spatial non-uniformity of pollution source emissions. In this paper, a Dual TV Plus machine vision algorithm is proposed, calculated and validated with the conventional Farneback optical flow algorithm in terms of industrial stack emission rates. In addition, the effect of turbulence on the inversion of SO2 emission under complex environmental conditions is evaluated by combining simulations and outdoor experiments. The results show that the Dual TV Plus algorithm performs better than the Farnebäck algorithm and the Dual TV-L1 algorithm, with 50% and 30% improvement in accuracy and 5% and 8% improvement in immunity to interference, respectively. This work is important for promoting the engineering applications of remote sensing instruments for environmental monitoring.
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