Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments

Estimating Plume Cloud (PC) height is essential for various applications, such as global climate models. Smokestack Plume Rise (PR) is the constant height at which the PC is carried downwind as its momentum dissipates and the PC and the ambient temperatures equalize. Although different parameterizations are used in most air-quality models to predict PR, they have yet to be verified thoroughly. This paper proposes a low-cost measurement technology to monitor smokestack PCs and make long-term, real-time measurements of PR. For this purpose, a two-stage method is developed based on Deep Convolutional Neural Networks (DCNNs). In the first stage, an improved Mask R-CNN, called Deep Plume Rise Network (DPRNet), is applied to recognize the PC. Here, image processing analyses and least squares, respectively, are used to detect PC boundaries and fit an asymptotic model into the boundaries centerline. The y-component coordinate of this model's critical point is considered PR. In the second stage, a geometric transformation phase converts image measurements into real-life ones. A wide range of images with different atmospheric conditions, including day, night, and cloudy/foggy, have been selected for the DPRNet training algorithm. Obtained results show that the proposed method outperforms widely-used networks in smoke border detection and recognition.