Visual smoke recognition based on an inverse-radiating attention pyramid network

Abstract

In thermal power generation systems, the exhaust gases produced during the process of power generation should be fully burnt before being discharged into the air through a high-altitude chimney, to help maintain safe production and to avoid pollution. A thermal power generation system will emit white smoke when operating normally; otherwise, under abnormal conditions: (1) large amounts of dark smoke may be released into the air due to incomplete combustion of the exhaust gases, leading to hazards to public health and the environment; (2) colorless smoke can be emitted if toxic exhaust gases are directly released into the air, also causing dangerous pollution. To address these issues, we have developed an inverse-radiating attention pyramid network (IRAP-Net) for visual smoke recognition, directed towards monitoring thermal power generation systems. The proposed IRAP-Net exploits various visual smoke features. IRAP-Net is based on three pyramid blocks, composed of 3, 4 and 5 convolution modules, respectively. An attention mechanism is introduced into each pyramid block selective of features. Lastly, an inverse-radiating connection converges all the outputs of the pyramid blocks in a feedforward manner, thereby merging low-level and high-level features. Experiments on a large smoke image database show that the proposed IRAP-Net achieves a recognition rate exceeding 95.5%, superior to other state-of-the-art (SOTA) models.