Research on object detection and recognition in remote sensing images based on YOLOv11
Abstract This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training e...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96314-x |
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| author | Lu-hao He Yong-zhang Zhou Lei Liu Wei Cao Jian-hua Ma |
| author_facet | Lu-hao He Yong-zhang Zhou Lei Liu Wei Cao Jian-hua Ma |
| author_sort | Lu-hao He |
| collection | DOAJ |
| description | Abstract This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map50 of 0.8920, a map50–95 of 0.8646, and an F1 score of 0.8709, highlighting the model’s high accuracy and robustness in addressing complex detection tasks. Furthermore, 80% of the test samples achieved confidence scores exceeding 85%, confirming the reliability of YOLOv11 in multiclass and multiobject detection scenarios. These findings suggest that YOLOv11 holds significant promise for remote sensing image target detection, demonstrating exceptional detection performance while offering robust technical support for intelligent remote sensing image analysis. Future studies will focus on expanding the dataset, refining the model architecture, and improving its performance in detecting small targets and processing complex scenes, paving the way for its broader applications in environmental protection, urban planning, and multiobject detection. |
| format | Article |
| id | doaj-art-66bacbda05bb4caf94af6d2e298a6af1 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-66bacbda05bb4caf94af6d2e298a6af12025-08-20T02:20:05ZengNature PortfolioScientific Reports2045-23222025-04-0115112510.1038/s41598-025-96314-xResearch on object detection and recognition in remote sensing images based on YOLOv11Lu-hao He0Yong-zhang Zhou1Lei Liu2Wei Cao3Jian-hua Ma4School of Earth Sciences and Engineering, Sun Yat-Sen UniversitySchool of Earth Sciences and Engineering, Sun Yat-Sen UniversitySchool of Earth Sciences and Engineering, Sun Yat-Sen UniversityHunan Shizhuyuan Nonferrous Metals Co., Ltd.School of Earth Sciences and Engineering, Sun Yat-Sen UniversityAbstract This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map50 of 0.8920, a map50–95 of 0.8646, and an F1 score of 0.8709, highlighting the model’s high accuracy and robustness in addressing complex detection tasks. Furthermore, 80% of the test samples achieved confidence scores exceeding 85%, confirming the reliability of YOLOv11 in multiclass and multiobject detection scenarios. These findings suggest that YOLOv11 holds significant promise for remote sensing image target detection, demonstrating exceptional detection performance while offering robust technical support for intelligent remote sensing image analysis. Future studies will focus on expanding the dataset, refining the model architecture, and improving its performance in detecting small targets and processing complex scenes, paving the way for its broader applications in environmental protection, urban planning, and multiobject detection.https://doi.org/10.1038/s41598-025-96314-xRemote sensingComputer visionDeep learningObject detectionYOLOv11 |
| spellingShingle | Lu-hao He Yong-zhang Zhou Lei Liu Wei Cao Jian-hua Ma Research on object detection and recognition in remote sensing images based on YOLOv11 Scientific Reports Remote sensing Computer vision Deep learning Object detection YOLOv11 |
| title | Research on object detection and recognition in remote sensing images based on YOLOv11 |
| title_full | Research on object detection and recognition in remote sensing images based on YOLOv11 |
| title_fullStr | Research on object detection and recognition in remote sensing images based on YOLOv11 |
| title_full_unstemmed | Research on object detection and recognition in remote sensing images based on YOLOv11 |
| title_short | Research on object detection and recognition in remote sensing images based on YOLOv11 |
| title_sort | research on object detection and recognition in remote sensing images based on yolov11 |
| topic | Remote sensing Computer vision Deep learning Object detection YOLOv11 |
| url | https://doi.org/10.1038/s41598-025-96314-x |
| work_keys_str_mv | AT luhaohe researchonobjectdetectionandrecognitioninremotesensingimagesbasedonyolov11 AT yongzhangzhou researchonobjectdetectionandrecognitioninremotesensingimagesbasedonyolov11 AT leiliu researchonobjectdetectionandrecognitioninremotesensingimagesbasedonyolov11 AT weicao researchonobjectdetectionandrecognitioninremotesensingimagesbasedonyolov11 AT jianhuama researchonobjectdetectionandrecognitioninremotesensingimagesbasedonyolov11 |