| Autor: |
Leon-Alcazar, Juan, Alnumay, Yazeed, Zheng, Cheng, Trigui, Hassane, Patel, Sahejad, Ghanem, Bernard |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Winter Conference on Applications of Computer Vision 2024 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading. |
| Databáze: |
arXiv |
| Externí odkaz: |
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