NRC-GAMMA: Introducing a Novel Large Gas Meter Image Dataset
Ashkan Ebadi, Patrick Paul, Sofia Auer, St\'ephane Tremblay

TL;DR
The paper introduces NRC-GAMMA, a large, high-quality dataset of gas meter images to facilitate the development of automated reading systems using AI and computer vision techniques.
Contribution
It provides a comprehensive, systematically labeled dataset of nearly 29,000 gas meter images, enabling research and development of automatic meter reading solutions.
Findings
First large-scale gas meter image dataset available.
High-quality annotations validated for accuracy.
Supports development of AI-based automatic reading systems.
Abstract
Automatic meter reading technology is not yet widespread. Gas, electricity, or water accumulation meters reading is mostly done manually on-site either by an operator or by the homeowner. In some countries, the operator takes a picture as reading proof to confirm the reading by checking offline with another operator and/or using it as evidence in case of conflicts or complaints. The whole process is time-consuming, expensive, and prone to errors. Automation can optimize and facilitate such labor-intensive and human error-prone processes. With the recent advances in the fields of artificial intelligence and computer vision, automatic meter reading systems are becoming more viable than ever. Motivated by the recent advances in the field of artificial intelligence and inspired by open-source open-access initiatives in the research community, we introduce a novel large benchmark dataset of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Image Enhancement Techniques
MethodsAttention Model
