Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm. Issue 12 (14th September 2020)

• Record Type: Journal Article
• Authors: Li, Qingrong; Liu, Hui; Zhang, Junpeng; Zeng, Pengfei
• Is Part Of: IET image processing. Volume 14:Issue 12(2020)
• Page Start: 2879
• Page End: 2889
• Publication Date: 2020-09-14
• Subjects: backpropagation -- image colour analysis -- smoke -- image segmentation -- air pollution control -- neural nets -- feature extraction -- image texture -- image classification -- computer vision -- environmental science computing; multicolour smoke -- target segmentation -- industrial smoke image -- LBPSCV -- computer vision technology -- colour information -- industrial production -- air quality -- industrial smoke emissions -- natural wind speed -- targeted smoke -- texture information -- local contrast information -- extracted texture features -- smoke emission monitoring videos -- segmented industrial smoke images -- local binary pattern silhouette coefficient variant; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1315
• Languages: English
• ISSNs: 1751-9659

Abstract

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.