HRM: An Intelligent Helmet Recognition Model in Complex Scenes
This paper presents an intelligent helmet recognition model in complex scenes based on YOLOv5. Firstly, in construction site projects, consider that the photograph which needs to be identified has numerous problems. For example, helmet’s pixels are too tiny to detect, or a large number of workers ma...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2022/1352775 |
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| _version_ | 1832544400735993856 |
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| author | Panbo He Chunxue Wu Rami Yared Yuanhao Ma |
| author_facet | Panbo He Chunxue Wu Rami Yared Yuanhao Ma |
| author_sort | Panbo He |
| collection | DOAJ |
| description | This paper presents an intelligent helmet recognition model in complex scenes based on YOLOv5. Firstly, in construction site projects, consider that the photograph which needs to be identified has numerous problems. For example, helmet’s pixels are too tiny to detect, or a large number of workers makes helmets appear densely. A SE-Net channel attention module is added in different parts of the network layer of the model, so that the improved model can pay more attention to the global variables and increase the detection performance of small target information and dense target information. In addition, this paper constructs a helmet data set based on projects and adds training samples of dense targets and long-range small targets. Finally, the modified mosaic data enhancement reduces the influence of redundant background on the model and improves the recognition accuracy of the tiny target. The experimental results show that in the project, the average accuracy of helmet detection reaches 92.82%. Compared with SSD, YOLOv3, and YOLOv5, the average accuracy of this algorithm is improved by 6.89%, 8.28%, and 2.44% and has strong generalization ability in dense scenes and small target scenes, which meets the accuracy requirements of helmet wearing detection in engineering applications. |
| format | Article |
| id | doaj-art-527d25e023f74d71a4c084e42ff43da1 |
| institution | Kabale University |
| issn | 1687-9139 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-527d25e023f74d71a4c084e42ff43da12025-02-03T10:25:25ZengWileyAdvances in Mathematical Physics1687-91392022-01-01202210.1155/2022/1352775HRM: An Intelligent Helmet Recognition Model in Complex ScenesPanbo He0Chunxue Wu1Rami Yared2Yuanhao Ma3School of Optical-Electrical and Computer EngineeringSchool of Optical-Electrical and Computer EngineeringSchool of Computer ScienceCollege of Information EngineeringThis paper presents an intelligent helmet recognition model in complex scenes based on YOLOv5. Firstly, in construction site projects, consider that the photograph which needs to be identified has numerous problems. For example, helmet’s pixels are too tiny to detect, or a large number of workers makes helmets appear densely. A SE-Net channel attention module is added in different parts of the network layer of the model, so that the improved model can pay more attention to the global variables and increase the detection performance of small target information and dense target information. In addition, this paper constructs a helmet data set based on projects and adds training samples of dense targets and long-range small targets. Finally, the modified mosaic data enhancement reduces the influence of redundant background on the model and improves the recognition accuracy of the tiny target. The experimental results show that in the project, the average accuracy of helmet detection reaches 92.82%. Compared with SSD, YOLOv3, and YOLOv5, the average accuracy of this algorithm is improved by 6.89%, 8.28%, and 2.44% and has strong generalization ability in dense scenes and small target scenes, which meets the accuracy requirements of helmet wearing detection in engineering applications.http://dx.doi.org/10.1155/2022/1352775 |
| spellingShingle | Panbo He Chunxue Wu Rami Yared Yuanhao Ma HRM: An Intelligent Helmet Recognition Model in Complex Scenes Advances in Mathematical Physics |
| title | HRM: An Intelligent Helmet Recognition Model in Complex Scenes |
| title_full | HRM: An Intelligent Helmet Recognition Model in Complex Scenes |
| title_fullStr | HRM: An Intelligent Helmet Recognition Model in Complex Scenes |
| title_full_unstemmed | HRM: An Intelligent Helmet Recognition Model in Complex Scenes |
| title_short | HRM: An Intelligent Helmet Recognition Model in Complex Scenes |
| title_sort | hrm an intelligent helmet recognition model in complex scenes |
| url | http://dx.doi.org/10.1155/2022/1352775 |
| work_keys_str_mv | AT panbohe hrmanintelligenthelmetrecognitionmodelincomplexscenes AT chunxuewu hrmanintelligenthelmetrecognitionmodelincomplexscenes AT ramiyared hrmanintelligenthelmetrecognitionmodelincomplexscenes AT yuanhaoma hrmanintelligenthelmetrecognitionmodelincomplexscenes |