Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation
Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree heig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3636 |
| _version_ | 1827666678490595328 |
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| author | Jiayin Song Yue Zhao Wenlong Song Hongwei Zhou Di Zhu Qiqi Huang Yiming Fan Chao Lu |
| author_facet | Jiayin Song Yue Zhao Wenlong Song Hongwei Zhou Di Zhu Qiqi Huang Yiming Fan Chao Lu |
| author_sort | Jiayin Song |
| collection | DOAJ |
| description | Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F<sub>1</sub> score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies. |
| first_indexed | 2024-03-10T01:55:05Z |
| format | Article |
| id | doaj.art-8666404053184de2bce316d025eb1f42 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:05Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8666404053184de2bce316d025eb1f422023-11-23T12:58:37ZengMDPI AGSensors1424-82202022-05-012210363610.3390/s22103636Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height EstimationJiayin Song0Yue Zhao1Wenlong Song2Hongwei Zhou3Di Zhu4Qiqi Huang5Yiming Fan6Chao Lu7Department of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaTree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F<sub>1</sub> score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.https://www.mdpi.com/1424-8220/22/10/3636tree height estimationequidistant projectiondeep learningfisheye image |
| spellingShingle | Jiayin Song Yue Zhao Wenlong Song Hongwei Zhou Di Zhu Qiqi Huang Yiming Fan Chao Lu Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation Sensors tree height estimation equidistant projection deep learning fisheye image |
| title | Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation |
| title_full | Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation |
| title_fullStr | Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation |
| title_full_unstemmed | Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation |
| title_short | Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation |
| title_sort | fisheye image detection of trees using improved yolox for tree height estimation |
| topic | tree height estimation equidistant projection deep learning fisheye image |
| url | https://www.mdpi.com/1424-8220/22/10/3636 |
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