Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control

TL;DR

This paper introduces a fast and accurate instance segmentation method for food crystal analysis in microscopic images, significantly improving efficiency over traditional manual counting methods.

Contribution

The paper presents an object detection-based segmentation approach that is five times faster than existing methods, with comparable accuracy, and proposes criteria for distinguishing crystals from mimics.

Findings

Achieves comparable crystal counting accuracy to existing segmentation methods.

Operates five times faster than previous approaches.

Provides criteria for separating crystals from hard mimics.

Abstract

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.