提出高效实例分割方法，实现食品晶体计数与尺寸分布快速精准预测。
Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control
- 基于目标检测设计轻量级实例分割框架，提升处理速度。
- 计数准确率与现有方法相当，推理速度提升5倍。
- 定义硬质伪影与晶体的区分标准，助力人工标注效率。
本文面向食品制造中的晶体质量控制场景，旨在高效预测食品晶体的数量与尺寸分布。以往厂商依赖显微图像中的人工计数，耗时且结果不一致。由于晶体形状多样且常被周围硬质伪影干扰，晶体分割极具挑战。为此，我们提出一种基于目标检测的高效实例分割方法。实验表明，该方法在晶体计数准确率上与现有分割方法相当，但推理速度提升五倍。此外，通过实验建立了区分硬质伪影与真实食品晶体的客观标准，可有效支持类似数据集的人工标注工作。
原文摘要 · Abstract (English)
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.
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