Recognition and monitoring of gas leakage using infrared imaging technique with machine learning
Guardado en:
| Publicado en: | Multimedia Tools and Applications vol. 83, no. 12 (Apr 2024), p. 35413 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Springer Nature B.V.
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3030965201 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1380-7501 | ||
| 022 | |a 1573-7721 | ||
| 024 | 7 | |a 10.1007/s11042-023-17131-w |2 doi | |
| 035 | |a 3030965201 | ||
| 045 | 2 | |b d20240401 |b d20240430 | |
| 084 | |a 108528 |2 nlm | ||
| 100 | 1 | |a Shirley, C. P. |u Karunya Institute of Technology and Sciences, Coimbatore, India (GRID:grid.412056.4) (ISNI:0000 0000 9896 4772) | |
| 245 | 1 | |a Recognition and monitoring of gas leakage using infrared imaging technique with machine learning | |
| 260 | |b Springer Nature B.V. |c Apr 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Gas leakage in the domestic sector leads to numerous dangerous hazards. The earlier prediction is one of the safety measures to prevent various consequences. The proposed system helps in the earlier detection of gas leakage using artificial intelligence techniques. This involves machine learning with infrared imaging techniques. Machine learning is the process of teaching machines to do tasks automatically by analysing and testing data. The obtained data are processed using image processing techniques. The image processing technique is used to extract information from the images involving various stages such as image enhancement and image analysis. The initial data are obtained in the form of images using infrared imaging techniques. It is the technique that utilizes the infrared portion of the electromagnetic spectrum to obtain the desired images. The obtained images are processed to obtain clear images in the dataset. The data is then tested and taught using machine learning evolving optimization techniques on the data. This helps in the accurate detection of gas leakage. To compare, the individual models' test accuracy ranged from 99.8% (based on Gas Sensor data using Random Forest) with the training accuracy of 99.8%. Experimental results demonstrate its ability to automatically detect and display gas leaks in high quality by establishing a background model, segmenting the gas-leak zone with motion characteristics, and rendering the gas-leak region in colour using grayscale mapping. | |
| 653 | |a Model accuracy | ||
| 653 | |a Data analysis | ||
| 653 | |a Imaging techniques | ||
| 653 | |a Machine learning | ||
| 653 | |a Image analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Image enhancement | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Gas sensors | ||
| 653 | |a Infrared imagery | ||
| 653 | |a Safety measures | ||
| 653 | |a Teaching machines | ||
| 653 | |a Infrared imaging | ||
| 653 | |a Image processing | ||
| 653 | |a Leakage | ||
| 700 | 1 | |a Raja, J Immanuel John |u Karunya Institute of Technology and Sciences, Coimbatore, India (GRID:grid.412056.4) (ISNI:0000 0000 9896 4772) | |
| 700 | 1 | |a Evangelin Sonia, S. V. |u Karunya Institute of Technology and Sciences, Coimbatore, India (GRID:grid.412056.4) (ISNI:0000 0000 9896 4772) | |
| 700 | 1 | |a Titus, I. |u Karunya Institute of Technology and Sciences, Coimbatore, India (GRID:grid.412056.4) (ISNI:0000 0000 9896 4772) | |
| 773 | 0 | |t Multimedia Tools and Applications |g vol. 83, no. 12 (Apr 2024), p. 35413 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3030965201/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3030965201/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |