Pointer meters recognition method in the wild based on innovative deep learning techniques
Abstract This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-81248-7 |
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| _version_ | 1841559694461632512 |
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| author | Jiajun Feng Haibo Luo Rui Ming |
| author_facet | Jiajun Feng Haibo Luo Rui Ming |
| author_sort | Jiajun Feng |
| collection | DOAJ |
| description | Abstract This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images. We also combine the output of the decoder network and the output of the improved CBAM as inputs to the Object Heatmap-Scalarmap Module to find pointer tip heat map peaks and predict pointer pointing. The method proposed in this paper is compared with several deep learning networks. The experimental results show that the model in this paper has the highest recognition correctness, with an average precision of 0.95 and 0.763 for Object Keypoint Similarity and Vector Direction Similarity, and an average recall of 0.951 and 0.856 in the test set, respectively, and achieves the best results in terms of efficiency and accuracy achieve the best trade-off, and performs well in recognizing multiple pointer targets. This demonstrates its robustness in real scenarios and provides a new idea for recognizing pointers in low-quality images more efficiently and accurately in complex industrial scenarios. |
| format | Article |
| id | doaj-art-d44fef5e874d41baa556596a4a4dcfb1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d44fef5e874d41baa556596a4a4dcfb12025-01-05T12:16:54ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-024-81248-7Pointer meters recognition method in the wild based on innovative deep learning techniquesJiajun Feng0Haibo Luo1Rui Ming2College of Computer and Information Sciences, Fujian Agriculture and Forestry UniversityCollege of Computer and Data Science, Minjiang UniversityCollege of Computer and Data Science, Minjiang UniversityAbstract This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images. We also combine the output of the decoder network and the output of the improved CBAM as inputs to the Object Heatmap-Scalarmap Module to find pointer tip heat map peaks and predict pointer pointing. The method proposed in this paper is compared with several deep learning networks. The experimental results show that the model in this paper has the highest recognition correctness, with an average precision of 0.95 and 0.763 for Object Keypoint Similarity and Vector Direction Similarity, and an average recall of 0.951 and 0.856 in the test set, respectively, and achieves the best results in terms of efficiency and accuracy achieve the best trade-off, and performs well in recognizing multiple pointer targets. This demonstrates its robustness in real scenarios and provides a new idea for recognizing pointers in low-quality images more efficiently and accurately in complex industrial scenarios.https://doi.org/10.1038/s41598-024-81248-7 |
| spellingShingle | Jiajun Feng Haibo Luo Rui Ming Pointer meters recognition method in the wild based on innovative deep learning techniques Scientific Reports |
| title | Pointer meters recognition method in the wild based on innovative deep learning techniques |
| title_full | Pointer meters recognition method in the wild based on innovative deep learning techniques |
| title_fullStr | Pointer meters recognition method in the wild based on innovative deep learning techniques |
| title_full_unstemmed | Pointer meters recognition method in the wild based on innovative deep learning techniques |
| title_short | Pointer meters recognition method in the wild based on innovative deep learning techniques |
| title_sort | pointer meters recognition method in the wild based on innovative deep learning techniques |
| url | https://doi.org/10.1038/s41598-024-81248-7 |
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