LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design
Abstract Small object detection in UAV aerial images is challenging due to low contrast, complex backgrounds, and limited computational resources. Traditional methods struggle with high miss detection rates and poor localization accuracy caused by information loss, weak cross-layer feature interacti...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07021-6 |
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| author | Yuqi Han Chengcheng Wang Hui Luo Huihua Wang Zaiqing Chen Yuelong Xia Lijun Yun |
| author_facet | Yuqi Han Chengcheng Wang Hui Luo Huihua Wang Zaiqing Chen Yuelong Xia Lijun Yun |
| author_sort | Yuqi Han |
| collection | DOAJ |
| description | Abstract Small object detection in UAV aerial images is challenging due to low contrast, complex backgrounds, and limited computational resources. Traditional methods struggle with high miss detection rates and poor localization accuracy caused by information loss, weak cross-layer feature interaction, and rigid detection heads. To address these issues, we propose LRDS-YOLO, a lightweight and efficient model tailored for UAV applications. The model incorporates a Light Adaptive-weight Downsampling (LAD) module to retain fine-grained small object features and reduce information loss. A Re-Calibration Feature Pyramid Network (Re-Calibration FPN) enhances multi-scale feature fusion using bidirectional interactions and resolution-aware hybrid attention. The SegNext Attention mechanism improves target focus while suppressing background noise, and the dynamic detection head (DyHead) optimizes multi-dimensional feature weighting for robust detection. Experiments show that LRDS-YOLO achieves 43.6% mAP50 on VisDrone2019, 11.4% higher than the baseline, with only 4.17M parameters and 24.1 GFLOPs, striking a balance between accuracy and efficiency. On the HIT-UAV infrared dataset, it reaches 84.5% mAP50, demonstrating strong generalization. With its lightweight design and high precision, LRDS-YOLO offers an effective real-time solution for UAV-based small object detection. |
| format | Article |
| id | doaj-art-c2c528748fe645c9a8229d9de2cb539d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c2c528748fe645c9a8229d9de2cb539d2025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-07021-6LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient designYuqi Han0Chengcheng Wang1Hui Luo2Huihua Wang3Zaiqing Chen4Yuelong Xia5Lijun Yun6School of Information, Yunnan Normal UniversitySchool of Information, Yunnan Normal UniversityKunming Branch of the Third College of PLA Information Engineering UniversityDepartment of Education of Yunnan Province, Engineering Research Center of Computer Vision and Intelligent Control TechnologySchool of Information, Yunnan Normal UniversitySchool of Information, Yunnan Normal UniversitySchool of Information, Yunnan Normal UniversityAbstract Small object detection in UAV aerial images is challenging due to low contrast, complex backgrounds, and limited computational resources. Traditional methods struggle with high miss detection rates and poor localization accuracy caused by information loss, weak cross-layer feature interaction, and rigid detection heads. To address these issues, we propose LRDS-YOLO, a lightweight and efficient model tailored for UAV applications. The model incorporates a Light Adaptive-weight Downsampling (LAD) module to retain fine-grained small object features and reduce information loss. A Re-Calibration Feature Pyramid Network (Re-Calibration FPN) enhances multi-scale feature fusion using bidirectional interactions and resolution-aware hybrid attention. The SegNext Attention mechanism improves target focus while suppressing background noise, and the dynamic detection head (DyHead) optimizes multi-dimensional feature weighting for robust detection. Experiments show that LRDS-YOLO achieves 43.6% mAP50 on VisDrone2019, 11.4% higher than the baseline, with only 4.17M parameters and 24.1 GFLOPs, striking a balance between accuracy and efficiency. On the HIT-UAV infrared dataset, it reaches 84.5% mAP50, demonstrating strong generalization. With its lightweight design and high precision, LRDS-YOLO offers an effective real-time solution for UAV-based small object detection.https://doi.org/10.1038/s41598-025-07021-6UAV (unmanned aerial vehicle)Small object detectionFeature pyramid networkReal timeAttention mechanisms |
| spellingShingle | Yuqi Han Chengcheng Wang Hui Luo Huihua Wang Zaiqing Chen Yuelong Xia Lijun Yun LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design Scientific Reports UAV (unmanned aerial vehicle) Small object detection Feature pyramid network Real time Attention mechanisms |
| title | LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design |
| title_full | LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design |
| title_fullStr | LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design |
| title_full_unstemmed | LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design |
| title_short | LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design |
| title_sort | lrds yolo enhances small object detection in uav aerial images with a lightweight and efficient design |
| topic | UAV (unmanned aerial vehicle) Small object detection Feature pyramid network Real time Attention mechanisms |
| url | https://doi.org/10.1038/s41598-025-07021-6 |
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