Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images
Brazil is the largest producer of oranges in the world and the automatic detection of fruits has been a challenging task in the context of remote sensing, due to variations in fruit appearance, changes in lighting and occlusions of foliage and neighboring fruits. In this sense, this paper focus on t...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-11-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-3-2024/303/2024/isprs-annals-X-3-2024-303-2024.pdf |
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| author | M. R. Oviedo Espinosa L. R. Porto V. S. W. Orlando A. M. G. Tommaselli A. P. Dal Poz N. N. Imai |
| author_facet | M. R. Oviedo Espinosa L. R. Porto V. S. W. Orlando A. M. G. Tommaselli A. P. Dal Poz N. N. Imai |
| author_sort | M. R. Oviedo Espinosa |
| collection | DOAJ |
| description | Brazil is the largest producer of oranges in the world and the automatic detection of fruits has been a challenging task in the context of remote sensing, due to variations in fruit appearance, changes in lighting and occlusions of foliage and neighboring fruits. In this sense, this paper focus on the detection of oranges in multispectral images, with different spectral bands and exposures, using a convolutional neural network (CNN) known as YOU ONLY LOOK ONCE (YOLO). The results indicate that, after 300 epochs, the model demonstrated an accuracy of 81.5% and an approximate recovery rate of 85%. Shutter speeds 1/640s and 1/250s are not suitable for detection due to low light and overexposure, respectively. Intermediate values may be more suitable for identifying a larger number of fruits. |
| format | Article |
| id | doaj-art-577388c2929e44c9a5f75acc76a5b1c3 |
| institution | OA Journals |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-577388c2929e44c9a5f75acc76a5b1c32025-08-20T02:18:39ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502024-11-01X-3-202430330810.5194/isprs-annals-X-3-2024-303-2024Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure ImagesM. R. Oviedo Espinosa0L. R. Porto1V. S. W. Orlando2A. M. G. Tommaselli3A. P. Dal Poz4N. N. Imai5São Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilSão Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilSão Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilSão Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilSão Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilSão Paulo State University (UNESP), Presidente Prudente, São Paulo, BrazilBrazil is the largest producer of oranges in the world and the automatic detection of fruits has been a challenging task in the context of remote sensing, due to variations in fruit appearance, changes in lighting and occlusions of foliage and neighboring fruits. In this sense, this paper focus on the detection of oranges in multispectral images, with different spectral bands and exposures, using a convolutional neural network (CNN) known as YOU ONLY LOOK ONCE (YOLO). The results indicate that, after 300 epochs, the model demonstrated an accuracy of 81.5% and an approximate recovery rate of 85%. Shutter speeds 1/640s and 1/250s are not suitable for detection due to low light and overexposure, respectively. Intermediate values may be more suitable for identifying a larger number of fruits.https://isprs-annals.copernicus.org/articles/X-3-2024/303/2024/isprs-annals-X-3-2024-303-2024.pdf |
| spellingShingle | M. R. Oviedo Espinosa L. R. Porto V. S. W. Orlando A. M. G. Tommaselli A. P. Dal Poz N. N. Imai Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images |
| title_full | Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images |
| title_fullStr | Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images |
| title_full_unstemmed | Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images |
| title_short | Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images |
| title_sort | evaluation of yolo efficiency in automatic orange detection in multi exposure images |
| url | https://isprs-annals.copernicus.org/articles/X-3-2024/303/2024/isprs-annals-X-3-2024-303-2024.pdf |
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