Speeding up the annotation process in semantic segmentation industrial applications

2026-06-18

AI Summary

This study addresses the challenge of time-consuming and error-prone manual annotation in semantic segmentation of high-resolution industrial images, such as steel microstructures. The authors propose an unsupervised computer vision pipeline to generate pre-annotations that significantly accelerate pixel-level labeling. For the first time, they quantitatively demonstrate that pre-annotation reduces per-image annotation time from 170 to 37 hours—a 78% reduction—while maintaining segmentation quality. As a key contribution, the work releases the largest publicly available, expert-validated steel microstructure segmentation dataset to date, already deployed in industrial settings, along with benchmark models. The dataset is distributed under the MIT license and assigned a persistent DOI to ensure long-term accessibility and reproducibility.

Abstract

Current machine learning models commonly require large and well-annotated datasets. However, the annotation process often becomes a bottleneck, with increased complexity leading to higher chances of human errors. Within this context, our goal in this paper is to leverage unsupervised algorithms to improve data annotation efficiency for complex semantic segmentation problems in industrial materials science. Previous research has quantified labeling time and others explored unsupervised methods. However, to the best of our knowledge, this is the first study to quantify how much unsupervised algorithms accelerate the labeling process. We aim to validate the extent to which this laborious process can be accelerated, focusing on semantic segmentation tasks that involve annotating each pixel of high-resolution images, such as the microstructure characterization challenge in materials science. Specifically, we demonstrate that by using unsupervised computer vision algorithms, the time required for the labeling process can be reduced from 170 hours to 37 hours, achieving an approximate reduction of 78\%. The dataset we work with includes large images of dimensions 1280x959 and 960x703, which further increases the complexity of the annotation task. Despite these challenges, we create and share the largest public steel microstructure segmentation dataset to date, available under MIT License with permanent DOI, contributing a fully annotated, high-resolution dataset to the field. Additionally, this is the first work to compare the labeling time from scratch (a common approach in previous studies) to the labeling time when using these unsupervised algorithms as a pre-annotation step. Furthermore, we provide a Deep Learning model trained on this dataset, validated by field experts, and deployed in an industrial setting, serving as an initial benchmark for this public dataset.

Problem

• semantic segmentation
• annotation efficiency
• industrial materials science
• pixel-level annotation
• labeling bottleneck

Innovation

• unsupervised pre-annotation
• semantic segmentation
• annotation acceleration
• materials microstructure
• industrial computer vision