GDI gasoline engine spray wall-impingement parameter automatic extraction method and system based on machine vision

Abstract

The invention discloses a GDI gasoline engine spray wall-impingement parameter automatic extraction method and system based on machine vision. The method comprises the following steps: step 1, background separation; 2, hierarchical binarization based on wavelet transform; 3, extraction of spray wall-impingement parameters: 3.1, extraction of a spray penetration distance L; 3.2, extraction of a spray cone angle theta; 3.3, extraction of a spraying radius r; and 3.4, extraction of a spray height h. The automatically extracted spray wall-impingement parameters can accurately reflect the spray wall-impingement development trend, the parameter values are basically consistent with those in a manual calibration result, the error is small, and the spray wall-impingement parameters can be automatically, rapidly and accurately extracted.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a machine vision-based automatic extraction method and system for GDI gasoline engine spray wall collision parameters. Background technique [0002] GDI gasoline engine has potential advantages in improving power performance, fuel economy and reducing harmful emissions [1-2] . However, the phenomenon of spray hitting the wall inevitably occurs during the injection process, which seriously affects the formation, combustion and emission of the mixed gas in the engine. [3-5] . Therefore, the study of spray wall impingement characteristics is very important, and it is one of the key factors affecting engine performance. In recent years, scholars at home and abroad have conducted a lot of research on spray and spray wall impact characteristics. [6-8] . In the research process, for a large number of images of spray impingement walls with complex structures and blurred boundaries unde...

AI Technical Summary

Problems solved by technology

During the research process, there is no effective method for how to quickly and accurately extract the characteristic parameters of spray images for a large number of images of spray impingement walls with complex structures and blurred boundaries under different conditions and at different acquisition times.

At present, at home and abroad, manual calibration or simple image processing methods are usually used to observe and measure the parameters of spray wall impingement. This method is not only time-consuming and laborious, but also greatly affected by human factors when defining the boundaries of different images. It is even more impossible to quickly and automatically process images and extract collision parameters

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