Prickly ash image recognition based on HSV and shape feature fusion

Changes in lighting conditions will have an impact on the target recognition rate of Zanthoxylum bungeanum maxim. It is related to whether machine vision technology can be effectively applied to Zanthoxylum bungeanum picking at the production site. Based on the analysis of HSV characteristic image recognition technology, a prickly ash recognition algorithm combining HSV and shape features is proposed. The calculation uses a homomorphic filtering method to compensate for the illumination, which solves the problem of low recognition rate of Zanthoxylum bungeanum due to uneven illumination. The roundness feature of Zanthoxylum bungeanum is used to eliminate the interference of branches and leaves and realize accurate recognition. The average recognition rate reaches 94.0%, which is higher than when the HSV characteristic recognition is used alone. Under backlight and shading conditions, the recognition rate is increased by 4%, 13%, and 21%, respectively, and the running time under shading conditions is shortened by 14.6%. This paper provides a method to improve the recognition rate of Zanthoxylum bungeanum under shade conditions.