A Multiple Light Scenes Suited Turbidity Analysis Method Based on Image Recognition and Information Fusion

Turbidity has been used as a significant indicator of water quality, so turbidity measurement is widely applied in sewage treatment and other fields. In the traditional measurement method of turbidity, a dark, closed measuring environment is required to reduce the interference of ambient light, which limits the application of turbidity measurement. To improve the adaptability of turbidity measurement to different light scenes, a multiple light scenes suited turbidity analysis method based on image recognition and information fusion is proposed. First, a turbidity image acquisition system is designed. After image preprocessing, prediction network groups for multiple light scenes are established, and two optimal prediction networks are adaptively selected according to different ambient light scenes, improving adaptability to multiple measuring environments. Second, to improve prediction accuracy, Dempster–Shafer (D–S) evidence theory is adopted to realize the information fusion of network prediction results. Three different light scenes of 0, 50, and 100 lx are built through experiments, and the results show that the accuracy of the proposed method in the three light scenes is above 95%, which demonstrates the adaptability to multiple light scenes and provides a new way of industrial online measurement.