IJmond Industrial Smoke Segmentation Dataset

2026-03-24

AI Summary

This work addresses the scarcity of high-quality industrial smoke images with pixel-level annotations in existing public datasets, which has hindered the development of vision-based detection and environmental monitoring algorithms. To bridge this gap, we construct and release a dedicated dataset tailored for semantic segmentation of industrial smoke, offering the first fine-grained pixel-level annotations specifically designed for industrial smoke scenarios. The dataset is built using standardized image acquisition and annotation protocols, ensuring clear structure and consistent labeling. It has been made publicly available via the figshare platform under a CC BY 4.0 license. By filling a critical void in publicly accessible resources, this contribution provides a foundational benchmark for future algorithm development and performance evaluation in industrial smoke analysis.

Technology Category

• Computer Vision: Segmentation
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Problem

• industrial smoke
• smoke segmentation
• dataset
• computer vision
• environmental monitoring

Innovation

• industrial smoke segmentation
• dataset
• computer vision
• environmental monitoring
• semantic segmentation