A visual inspection method for defect screening of dried blueberry

Abstract

This invention discloses a visual detection method for defect screening of dried blueberries, specifically relating to the field of visual inspection technology for agricultural products. The method involves acquiring real-time images of the dried blueberries to be tested and segmenting them into individual images; inputting the individual images into a lightweight neural network model to extract basic feature maps; and calculating an image complexity index that fuses content complexity and context complexity in real time based on the basic feature maps; comparing the image complexity index with a preset threshold; dynamically selecting different inference paths and outputting corresponding inference results; this invention dynamically allocates computational resources through the image complexity index, enabling rapid classification of simple images and fine segmentation of complex images, significantly reducing average computation time while ensuring detection accuracy, thus solving the problem of high-precision models being unable to detect in real time; achieving accurate quantification of detection difficulty through the fusion of content complexity and context complexity; and employing a lightweight model design to reduce hardware costs, meeting the speed requirements of industrial sorting.

Images