Abstract
Chemical Oxygen Demand (COD) serves as a crucial metric for assessing the extent of water pollution attributable to organic substances. This study introduces an innovative approach for the detection of low-concentration COD in aqueous environments through the application of Laser-Induced Fluorescence (LIF) image processing. The technique employs an image sensor to capture fluorescence image data generated by organic compounds in water when excited by ultraviolet laser radiation. Subsequently, the COD value, indicative of the concentration of organic matter in the water, is derived via image processing techniques. Utilizing this methodology, an LIF image processing COD detection system has been developed. The system is primarily composed of a CMOS image sensor, an STM32 microprocessor, a laser emission module, and a display module. In this study, the system was employed to detect mixed solutions of sodium humate and glucose at varying concentrations, resulting in the acquisition of corresponding fluorescence images. By isolating color channels and processing the image data features, variations in RGB color characteristics were analyzed. The Partial Least Squares Regression (PLSR) analysis method was utilized to develop a predictive model for COD concentration values based on the average RGB color feature values from the characteristic regions of the fluorescence images. Within the COD concentration range of 0-12 mg/L, the system demonstrated a detection relative error of less than 10%. In summary, the system designed in this research, utilizing the LIF image processing method, exhibits high sensitivity, robust stability, miniaturization, and non-contact detection capabilities for low-concentration COD measurement. It is well-suited for rapid, real-time online water quality monitoring.