Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion
UAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed d...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2235 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850115538290737152 |
|---|---|
| author | Xingyu Di Kangning Cui Rui-Feng Wang |
| author_facet | Xingyu Di Kangning Cui Rui-Feng Wang |
| author_sort | Xingyu Di |
| collection | DOAJ |
| description | UAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed detections, thus reducing performance. To solve these problems, this study proposes a lightweight and high-precision model UAV-YOLO based on YOLOv8s. Firstly, a double separation convolution (DSC) module is designed to replace the Bottleneck structure in the C2f module with deep separable convolution and point-by-point convolution fusion, which can reduce the model parameters and calculation complexity while enhancing feature expression. Secondly, a new SPPL module is proposed, which combines spatial pyramid pooling rapid fusion (SPPF) with long-distance dependency modeling (LSKA) to improve the robustness of the model to multi-scale targets through cross-level feature association. Then, DyHead is used to replace the original detector head, and the discrimination ability of small targets in complex background is enhanced by adaptive weight allocation and cross-scale feature optimization fusion. Finally, the WIPIoU loss function is proposed, which integrates the advantages of Wise-IoU, MPDIoU and Inner-IoU, and incorporates the geometric center of bounding box, aspect ratio and overlap degree into a unified measure to improve the localization accuracy of small targets and accelerate the convergence. The experimental results on the VisDrone2019 dataset showed that compared to YOLOv8s, UAV-YOLO achieved an 8.9% improvement in the recall of mAP@0.5 and 6.8%, while the parameters and calculations were reduced by 23.4% and 40.7%, respectively. Additional evaluations of the DIOR, RSOD, and NWPU VHR-10 datasets demonstrate the generalization capability of the model. |
| format | Article |
| id | doaj-art-d49caae5cd314f52aa17adb1f93dbab7 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d49caae5cd314f52aa17adb1f93dbab72025-08-20T02:36:33ZengMDPI AGRemote Sensing2072-42922025-06-011713223510.3390/rs17132235Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature FusionXingyu Di0Kangning Cui1Rui-Feng Wang2School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, ChinaDepartment of Mathematics, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong KongCollege of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, ChinaUAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed detections, thus reducing performance. To solve these problems, this study proposes a lightweight and high-precision model UAV-YOLO based on YOLOv8s. Firstly, a double separation convolution (DSC) module is designed to replace the Bottleneck structure in the C2f module with deep separable convolution and point-by-point convolution fusion, which can reduce the model parameters and calculation complexity while enhancing feature expression. Secondly, a new SPPL module is proposed, which combines spatial pyramid pooling rapid fusion (SPPF) with long-distance dependency modeling (LSKA) to improve the robustness of the model to multi-scale targets through cross-level feature association. Then, DyHead is used to replace the original detector head, and the discrimination ability of small targets in complex background is enhanced by adaptive weight allocation and cross-scale feature optimization fusion. Finally, the WIPIoU loss function is proposed, which integrates the advantages of Wise-IoU, MPDIoU and Inner-IoU, and incorporates the geometric center of bounding box, aspect ratio and overlap degree into a unified measure to improve the localization accuracy of small targets and accelerate the convergence. The experimental results on the VisDrone2019 dataset showed that compared to YOLOv8s, UAV-YOLO achieved an 8.9% improvement in the recall of mAP@0.5 and 6.8%, while the parameters and calculations were reduced by 23.4% and 40.7%, respectively. Additional evaluations of the DIOR, RSOD, and NWPU VHR-10 datasets demonstrate the generalization capability of the model.https://www.mdpi.com/2072-4292/17/13/2235YOLOsmall object detectionUAV aerial photographylightweight model |
| spellingShingle | Xingyu Di Kangning Cui Rui-Feng Wang Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion Remote Sensing YOLO small object detection UAV aerial photography lightweight model |
| title | Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion |
| title_full | Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion |
| title_fullStr | Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion |
| title_full_unstemmed | Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion |
| title_short | Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion |
| title_sort | toward efficient uav based small object detection a lightweight network with enhanced feature fusion |
| topic | YOLO small object detection UAV aerial photography lightweight model |
| url | https://www.mdpi.com/2072-4292/17/13/2235 |
| work_keys_str_mv | AT xingyudi towardefficientuavbasedsmallobjectdetectionalightweightnetworkwithenhancedfeaturefusion AT kangningcui towardefficientuavbasedsmallobjectdetectionalightweightnetworkwithenhancedfeaturefusion AT ruifengwang towardefficientuavbasedsmallobjectdetectionalightweightnetworkwithenhancedfeaturefusion |