Research on the Optimal Cross-Sectional Image Acquisition Method of Sintering Machine Based on Brightness Difference

A high-precision feature extraction method for the tail image of a sintering machine was proposed based on machine vision technology to address the problem of high-precision feature extraction. The optimal cross-sectional image of the sintering machine was obtained based on brightness difference. The sintering process image was collected in real time by an industrial camera, and the red component information was extracted for grayscale processing. A fuzzy processing algorithm was designed to remove irrelevant information and noise. By analyzing the brightness of the tail section sequence, the brightness difference threshold was used as an important basis for image extraction, and the feature parameters of the red fire layer and porosity were extracted. Through experimental verification, the method proposed in this article had outstanding advantages such as high recognition accuracy, low misjudgment rate, and good real-time performance compared to conventional optimal tail section discrimination methods. It would provide real-time data support for precise control of sintering state.