Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm
Abstract In the process of feeding the distilling bucket after vapor detection, the existing methods can only realize the lag detection after the steam overflow, and can not accurately detect the location of the steam, etc. At the same time, in order to effectively reduce the occupancy of the comput...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-64289-w |
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| _version_ | 1832571678806245376 |
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| author | Xiaolian LIU Shaopeng Gong Xiangxu Hua Taotao Chen Chunjiang Zhao |
| author_facet | Xiaolian LIU Shaopeng Gong Xiangxu Hua Taotao Chen Chunjiang Zhao |
| author_sort | Xiaolian LIU |
| collection | DOAJ |
| description | Abstract In the process of feeding the distilling bucket after vapor detection, the existing methods can only realize the lag detection after the steam overflow, and can not accurately detect the location of the steam, etc. At the same time, in order to effectively reduce the occupancy of the computational resources and improve the deployment performance, this study established infrared image dataset of fermented grains surface, and fused the YOLO v5n and the knowledge distillation and the model pruning algorithms, and an lightweight method YOLO v5ns-DP was proposed as as a model for detecting temperature changes in the surface layer of fermented grains during the process of feeding the distilling. The experimental results indicated that the improvement makes YOLOv5n improve its performance in all aspects. The number of parameters, GLOPs and model size of YOLO v5ns-DP have been reduced by 28.6%, 16.5%, and 26.4%, respectively, and the mAP has been improved by 0.6. Therefore, the algorithm is able to predict in advance and accurately detect the location of the liquor vapor, which effectively improves the precision and speed of the detection of the temperature of the surface fermented grains , and well completes the real-time detecting task. |
| format | Article |
| id | doaj-art-b8c25f3c145247ac9fdaa86e32630589 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b8c25f3c145247ac9fdaa86e326305892025-02-02T12:25:09ZengNature PortfolioScientific Reports2045-23222024-06-0114111210.1038/s41598-024-64289-wResearch on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithmXiaolian LIU0Shaopeng Gong1Xiangxu Hua2Taotao Chen3Chunjiang Zhao4School of Mechanical Engineering, Taiyuan University of Science and TechnologySchool of Mechanical Engineering, Taiyuan University of Science and TechnologySchool of Mechanical Engineering, Taiyuan University of Science and TechnologySchool of Mechanical Engineering, Taiyuan University of Science and TechnologySchool of Mechanical Engineering, Taiyuan University of Science and TechnologyAbstract In the process of feeding the distilling bucket after vapor detection, the existing methods can only realize the lag detection after the steam overflow, and can not accurately detect the location of the steam, etc. At the same time, in order to effectively reduce the occupancy of the computational resources and improve the deployment performance, this study established infrared image dataset of fermented grains surface, and fused the YOLO v5n and the knowledge distillation and the model pruning algorithms, and an lightweight method YOLO v5ns-DP was proposed as as a model for detecting temperature changes in the surface layer of fermented grains during the process of feeding the distilling. The experimental results indicated that the improvement makes YOLOv5n improve its performance in all aspects. The number of parameters, GLOPs and model size of YOLO v5ns-DP have been reduced by 28.6%, 16.5%, and 26.4%, respectively, and the mAP has been improved by 0.6. Therefore, the algorithm is able to predict in advance and accurately detect the location of the liquor vapor, which effectively improves the precision and speed of the detection of the temperature of the surface fermented grains , and well completes the real-time detecting task.https://doi.org/10.1038/s41598-024-64289-wLightweight modelYOLO v5nKnowledge DistillationModel Pruning |
| spellingShingle | Xiaolian LIU Shaopeng Gong Xiangxu Hua Taotao Chen Chunjiang Zhao Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm Scientific Reports Lightweight model YOLO v5n Knowledge Distillation Model Pruning |
| title | Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| title_full | Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| title_fullStr | Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| title_full_unstemmed | Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| title_short | Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| title_sort | research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm |
| topic | Lightweight model YOLO v5n Knowledge Distillation Model Pruning |
| url | https://doi.org/10.1038/s41598-024-64289-w |
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