YOLO-SWIFT: A Small-object Wavelet-Integrated Feature Transform Network for Object Detection in UAV Aerial Imagery
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Abstract
Accurate detection of small-scale targets in UAV aerial imagery remains challenging due to severe scale variation, background interference, and feature degradation caused by repeated downsampling. To address these issues, this study proposes YOLO-SWIFT, a wavelet-integrated feature transformation network designed for small-object detection. First, a Position-Aware Coordinate Downsampling module is developed to preserve critical spatial information during feature compression through coordinate attention and skip connections. Second, a High-Resolution Feature Aggregation Network is introduced to establish an additional high-resolution detection branch for enhanced cross-scale feature fusion. Third, a Wavelet Bottleneck Enhancement module incorporating multi-level wavelet decomposition and a High-Frequency Retention pathway is designed to improve fine-detail representation while expanding the effective receptive field. Finally, an Adaptive Scale-aware Regression IoU loss function is proposed to dynamically balance localization and shape-consistency constraints for small targets. Experimental evaluation on the VisDrone2019 benchmark demonstrates that YOLO-SWIFT achieves 38.7% mAP50 and 22.5% mAP50:95. The proposed framework provides an effective solution for intelligent aerial sensing and offers potential applications in electromagnetic imaging, remote sensing interpretation, and autonomous surveillance systems.
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References
T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on convolutional neural networks (CNN) in vegetation remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, pp. 24-49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
B. F. Spencer, V. Hoskere, and Y. Narazaki, “Advances in computer vision-based civil infrastructure inspection and monitoring,” Engineering, vol. 5, no. 2, pp. 199-222, 2019, doi: 10.1016/j.eng.2018.11.030.
B. Mishra, D. Garg, P. Narang, and V. Mishra, “Drone-surveillance for search and rescue in natural disaster,” Computer Communications, vol. 156, pp. 1-10, 2020, doi: 10.1016/j.comcom.2020.03.012.
C. Lyu, W. Zhang, H. Huang, et al., “RTMDet: An empirical study of designing real-time object detectors,” arXiv preprint arXiv:2212.07784, 2022.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: Deformable transformers for end-to-end object detection,” In International Conference on Learning Representations, 2020.
Y. Tang, K. Han, J. Guo, C. Xu, C. Xu, and Y. Wang, “GhostNetV2: Enhance cheap operation with long-range attention,” Advances in Neural Information Processing Systems, vol. 35, pp. 9969-9982, 2023, doi: 10.52202/068431-0724.
C. Wang, W. He, Y. Nie, J. Guo, C. Liu, K. Han, and Y. Wang, “Gold-YOLO: Efficient object detector via gather-and-distribute mechanism,” Advances in Neural Information Processing Systems, vol. 36, pp. 51094-51106, 2023, doi: 10.52202/075280-2224.
Z. Chen, C. Yang, Q. Li, F. Zhao, Z. J. Zha, and F. Wu, “Disentangle your dense object detector,” In Proceedings of the 31st ACM International Conference on Multimedia, pp. 4939-4950, 2023, doi: 10.1145/3474085.3475351.
X. Zhu, S. Lyu, X. Wang, and Q. Zhao, “TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios,” In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 2778-2788, 2021, doi: 10.1109/ICCVW54120.2021.00312.
P. Zhu, L. Wen, X. Bian, H. Ling, and Q. Hu, “Vision meets drones: A challenge,” arXiv preprint arXiv:1804.07437, 2018.
T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125, 2017, doi: 10.1109/CVPR.2017.106.
S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” In Proceedings of the European Conference on Computer Vision, pp. 3-19, 2018, doi: 10.1007/978-3-030-01234-2_1.
Z. Liu, Y. Lin, Y. Cao, et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012-10022, 2021, doi: 10.1109/ICCV48922.2021.00986.
A. Howard, M. Sandler, G. Chu, et al., “Searching for MobileNetV3,” In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314-1324, 2019, doi: 10.1109/ICCV.2019.00140.
S. E. Finder, R. Amoyal, E. Treister, and O. Freifeld, “Wavelet convolutions for large receptive fields,” In European Conference on Computer Vision, pp. 351-367. Springer, 2024, doi: 10.1007/978-3-031-72949-2_21.
X. Li, W. Wang, L. Wu, et al., “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” Advances in Neural Information Processing Systems, vol. 33, pp. 21002-21012, 2020.
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759-8768, 2018, doi: 10.1109/CVPR.2018.00913.
Han Y, Wang C, Luo H,et al.LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design[J].SCIENTIFIC REPORTS, vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-07021-6.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018, doi: 10.1109/CVPR.2018.00745.
P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, “Multi-level wavelet-CNN for image restoration,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 773-782, 2019, doi: 10.1109/CVPRW.2018.00121.
C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475, 2023, doi: 10.1109/CVPR52729.2023.00721.
A. Wang, H. Chen, L. Liu, et al., “YOLOv10: Real-time end-to-end object detection,” arXiv preprint arXiv:2405.14458, 2024.