A Review of Turbidity Detection Based on Computer Vision

• Published in: IEEE access Vol. 6; pp. 60586 - 60604
• Main Authors: Liu, Yeqi, Chen, Yingyi, Fang, Xiaomin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: application; Computational modeling; Computer vision; Feature extraction; Imaging; imaging system; Instruments; Scattering; Turbidity; turbidity detection; water quality
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2875071

Computer vision technology has made great progress in practice in recent years, and it also has broad application prospects in turbidity detection. Turbidity detection plays an important role in water environment science, but popular turbidity detection methods have some limitations in aspects of cost, convenience, and space-time coverage. Based on above reasons, researchers are devoted to developing image-based turbidity detection methods as a complementary or even alternative to the popular turbidity detection method. However, the use of computer vision technology to detect turbidity is affected by many factors such as imaging system, feature extraction, model selection, and so on. Currently, there is no comparison and analysis of these methods in a framework. Therefore, this paper introduces typical turbidity detection methods based on computer vision in detail, with their principle, measurement range, accuracy, technical framework, and comparison. In this paper, existing studies are divided into four types according to different image sources, and seven image features mainly used in these studies are pointed out. The objective of this paper is to review the development status, existing problems, future research directions of image-based turbidity detection methods, and establishment of a unified framework which includes principles, technical framework, and main equipment of imaging systems.