Research on antenna dip angle recognition method based on improved YOLOv5
In order to achieve efficient and accurate measurement of antenna dip angle and meet the large-scale and efficient measurement requirements in wireless optimization operation and maintenance scenarios, the YOLOv5 target detection framework was cleverly applied in the complex scenario of antenna dip...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-10-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024223/ |
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| _version_ | 1841528870967181312 |
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| author | ZHANG Ning PAN Feng GENG Lujing CHEN Zuhao XU Tingting |
| author_facet | ZHANG Ning PAN Feng GENG Lujing CHEN Zuhao XU Tingting |
| author_sort | ZHANG Ning |
| collection | DOAJ |
| description | In order to achieve efficient and accurate measurement of antenna dip angle and meet the large-scale and efficient measurement requirements in wireless optimization operation and maintenance scenarios, the YOLOv5 target detection framework was cleverly applied in the complex scenario of antenna dip angle measurement, and it was improved to make it suitable for complex antenna detection and attitude recognition tasks, and accurately predict the dip angle. Experimental results show that the improved YOLOv5 model has the same detection capability as the original version, while its downdip prediction error is reduced by 13%, and the absolute prediction error is 0.635°. The improved YOLOv5 model not only guarantees high accuracy, but also significantly improves the measurement accuracy of antenna dip angle, providing a new technical path and reference basis for wireless optimization intelligent operation and maintenance. |
| format | Article |
| id | doaj-art-9453b54426224e128a80f34072599ad6 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-9453b54426224e128a80f34072599ad62025-01-15T03:34:09ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-10-014017318176757236Research on antenna dip angle recognition method based on improved YOLOv5ZHANG NingPAN FengGENG LujingCHEN ZuhaoXU TingtingIn order to achieve efficient and accurate measurement of antenna dip angle and meet the large-scale and efficient measurement requirements in wireless optimization operation and maintenance scenarios, the YOLOv5 target detection framework was cleverly applied in the complex scenario of antenna dip angle measurement, and it was improved to make it suitable for complex antenna detection and attitude recognition tasks, and accurately predict the dip angle. Experimental results show that the improved YOLOv5 model has the same detection capability as the original version, while its downdip prediction error is reduced by 13%, and the absolute prediction error is 0.635°. The improved YOLOv5 model not only guarantees high accuracy, but also significantly improves the measurement accuracy of antenna dip angle, providing a new technical path and reference basis for wireless optimization intelligent operation and maintenance.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024223/antenna dip angletarget detectiondeep learningimage signal processing |
| spellingShingle | ZHANG Ning PAN Feng GENG Lujing CHEN Zuhao XU Tingting Research on antenna dip angle recognition method based on improved YOLOv5 Dianxin kexue antenna dip angle target detection deep learning image signal processing |
| title | Research on antenna dip angle recognition method based on improved YOLOv5 |
| title_full | Research on antenna dip angle recognition method based on improved YOLOv5 |
| title_fullStr | Research on antenna dip angle recognition method based on improved YOLOv5 |
| title_full_unstemmed | Research on antenna dip angle recognition method based on improved YOLOv5 |
| title_short | Research on antenna dip angle recognition method based on improved YOLOv5 |
| title_sort | research on antenna dip angle recognition method based on improved yolov5 |
| topic | antenna dip angle target detection deep learning image signal processing |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024223/ |
| work_keys_str_mv | AT zhangning researchonantennadipanglerecognitionmethodbasedonimprovedyolov5 AT panfeng researchonantennadipanglerecognitionmethodbasedonimprovedyolov5 AT genglujing researchonantennadipanglerecognitionmethodbasedonimprovedyolov5 AT chenzuhao researchonantennadipanglerecognitionmethodbasedonimprovedyolov5 AT xutingting researchonantennadipanglerecognitionmethodbasedonimprovedyolov5 |