Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control
This work addresses a domain-specific problem for food manufacturing by providing an efficient solution for quality control, though it is incremental as it builds on existing object detection methods.
The paper tackles the problem of automating food crystal counting and size distribution analysis from microscopic images to replace manual methods, achieving comparable accuracy to existing segmentation methods while being five times faster.
This paper is directed towards the food crystal quality control area for manufacturing, focusing on efficiently predicting food crystal counts and size distributions. Previously, manufacturers used the manual counting method on microscopic images of food liquid products, which requires substantial human effort and suffers from inconsistency issues. Food crystal segmentation is a challenging problem due to the diverse shapes of crystals and their surrounding hard mimics. To address this challenge, we propose an efficient instance segmentation method based on object detection. Experimental results show that the predicted crystal counting accuracy of our method is comparable with existing segmentation methods, while being five times faster. Based on our experiments, we also define objective criteria for separating hard mimics and food crystals, which could benefit manual annotation tasks on similar dataset.