Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm

Abstract

The use of computer vision technology to analyse the characteristics of smoke such as Ringelmann blackness coefficient and colour information can directly and efficiently reflect the situation of smoke emissions in industrial production, which has great significance in improving air quality. As many factors stand in the way, including the amount and speed of industrial smoke emissions, natural wind speed, illumination etc., an accurate and complete detection of the targeted smoke in images becomes a difficult issue in this field. In this study, a local binary pattern Silhouettes coefficient variant (LBPSCV) is proposed to segment industrial smoke images. The variant of Silhouettes coefficient was used as the weight when calculating the local binary pattern (LBP) feature vector in the LBPSCV. The algorithm overcame the shortcoming that the texture information described by LBP lacks local contrast information, making the extracted texture features more easily to be distinguished between smoke and non-smoke images. Smoke emission monitoring videos with different characteristics have been used in experiments, such as smoke emission videos with low light, multiple chimney exhaust, multi-colour smoke etc. The results show that the proposed method has higher detection accuracy and a lower false-positive rate.