Abstract
Unmanned Aerial Vehicle (UAV) image object detection plays a crucial role in various fields. However, compared with natural images, UAV images are characterized by significant target scale variations, complex backgrounds, dense small targets, and clustered target distributions, which pose serious challenges to object detection tasks. To address these issues, this study proposes an improved object detection algorithm named MFRA-YOLO, which combines multi-scale feature fusion and receptive-field attention-based convolution, built upon the baseline YOLOv8n algorithm. First, Monte Carlo attention is integrated into receptive-field attention-based convolution to enhance cross-scale information interaction capability, thereby forming a novel convolutional module for downsampling operations. Second, a multi-scale selective fusion module is incorporated into the feature fusion network to enable adaptive cross-scale integration of features. When coupled with the scale sequence feature fusion module, this integration significantly enhances the detection performance for small targets. Finally, the Focaler-PIoUv2 loss function is designed to replace the CIoU in the baseline algorithm. This replacement allows the algorithm to better balance hard and easy samples and improve detection accuracy. Experimental results on the public dataset VisDrone2019 show that MFRA-YOLO achieves superior accuracy-efficiency tradeoffs compared to the baseline YOLOv8n and other YOLO variants. Compared to YOLOv8n, MFRA-YOLO improves mAP50 by 3.5% and mAP50:95 by 2.3%, respectively. These performance gains are accompanied by only modest increases in parameter count and computational cost. Notably, while maintaining performance at 143 FPS, thereby satisfying the real-time deployment requirements for UAVs. Furthermore, compared with several state-of-the-art algorithms designed for UAV scenarios, MFRA-YOLO also offers distinct advantages. To verify the generalization ability and stability of MFRA-YOLO, comparative experiments are carried out on the RSOD dataset, where our algorithm consistently demonstrates excellent detection performance. Overall, these results confirm that MFRA-YOLO not only improves the detection performance for UAV imagery substantially but also achieves an excellent balance between detection accuracy and efficiency.
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References
Lei, T., Li, C. & He, X. Application of aerial remote sensing of pilotless aircraft to disaster emergency rescue. J. Nat. Disasters. 20 (1), 178–183 (2021).
Hu, Y., Jing, W., Yang, C. & Shu, S. Review of coastal ecological environment monitoring based on unmanned aerial vehicle remote sensing. Bull. Surveying Mapp. 1 (6), 18–24 (2022).
Byun, S., Shin, I. K., Moon, J., Kang, J. & Choi, S. I. Road traffic monitoring from UAV images using deep learning networks. Remote Sens. 13 (20), 4027 (2021).
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: unified, real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 2016, 779–788 (2016).
Redmon, J. & Farhadi, A. YOLO9000: better, faster, stronger. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 2017, 6517–6525 (2017).
Redmon, J. & Farhadi, A. YOLOv3: An incremental improvement. Preprint at https://arxiv.org/abs/1804.02767 (2018).
Jocher, G. Ultralytics YOLOv5. https://github.com/ultralytics/YOLOv5/tree/v7.0 (2020).
Wang, C., Bochkovskiy, A. & Liao, H. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 7464–7475 (2023).
Jocher, G., Chaurasia, A. & Qiu, J. Ultralytics YOLOv8. https://github.com/ultralytics/ultralytics (2023).
Liu, W. et al. SSD: single shot multibox detector. Comput. Vision—ECCV. 9905, 27–31 (2016).
Wang, G. et al. UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAVaerial photography scenarios. Sensors 23 (16), 7190 (2023).
Wang, H. et al. A remote sensing image target detection algorithm based on improved YOLOv8. Appl. Sci. 14 (4), 1557 (2024).
Qing, Y., Liu, W., Feng, L. & Gao, W. Improved YOLO network for free-angle remote sensing target detection. Remote Sens. 13 (11), 2171 (2021).
Zhang, Z. & Drone-YOLO An efficient neural network method for object detection in drone images. Drones 7 (8), 526 (2023).
Meng, X., Yuan, F. & Zhang, D. Improved model MASW YOLO for small target detection in UAV images based on YOLOv8. Sci. Rep. 15, 25027 (2025).
Lai, H., Chen, L., Liu, W., Yan, Z. & Ye, S. STC-YOLO: small object detection network for traffic signs in complex environments. Sensors 23 (11), 5307 (2023).
Jiang, X., Cui, Q. & Wang, C. A model for infrastructure detection along highways based on remote sensing images from UAVs. Sensors 23 (8), 3847 (2023).
Liu, Z., Gao, Y., Du, Q., Chen, M. & Lv, W. YOLO-extract: improved YOLOv5 for aircraft object detection in remote sensing images. IEEE Access. 11, 1742–1751 (2023).
Cao, X., Zhang, Y., Lang, S. & Gong, Y. Swin-transformer-based YOLOv5 for small-object detection in remote sensing images. Sensors 23 (7), 3634 (2023).
Shen, J. et al. An anchor-free lightweight deep convolutional network for vehicle detection in aerial images. IEEE Trans. Intell. Transp. Syst. 23 (12), 24330–24342 (2022).
Xiao, L., Li, W., Yao, S., Liu, H. & Ren, D. High-precision and lightweight small-target detection algorithm for low-cost edge intelligence. Sci. Rep. 14, 23542 (2024).
Wang, D., Gao, Z., Fang, J., Li, Y. & Xu, Z. Improving UAV aerial imagery detection method via superresolution synergy. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 18, 3959–3972 (2025).
Xiao, L. et al. EDet-YOLO: an efficient small object detection algorithm for aerial images. Real-Time Image Proc. 22, 175 (2025).
Lin, T. Y. et al. Feature pyramid networks for object detection. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 2017, 2117–2125 (2017).
Liu, S., Qi, L., Qin, H., Shi, J. & Jia, J. Path aggregation network for instance segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 8759–8768 (2018).
Dai, W. et al. Exploiting scale-variant attention for segmenting small medical objects. Preprint at https://arxiv.org/abs/2407.07720 (2024).
Zhang, X. et al. RFAConv: Innovating spatial attention and standard convolutional operation. Preprint at https://arxiv.org/abs/2304.03198 (2023).
Xie, L. et al. SHISRCNet: Super-resolution and classification network for low-resolution breast cancer histopathology image. Preprint at https://arxiv.org/abs/2306.14119v1 (2023).
Kang, M., Ting, C., Ting, F. & Phan, R. C. W. ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation. Image Vis. Comput. 147, 105057 (2024).
Liu, C. et al. Powerful-IoU: more straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism. Neural Netw. 170, 276–284 (2024).
Zhang, H., Zhang, S. & Focaler-IoU More focused intersection over union loss. Preprint at https://arxiv.org/abs/2401.10525 (2024).
Zhu, P. et al. Detection and tracking Meet drones challenge. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11), 7380–7399 (2021).
Long, Y., Gong, Y., Xiao, Z. & Liu, Q. Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 44 (5), 2486–2498 (2017).
Ren, S. et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39 (6), 1137–1149 (2017).
Zhao, Y. et al. DETRs beat YOLOs on real-time object detection. Preprint at https://arxiv.org/abs/2304.08069 (2023).
Li, C. et al. YOLOv6: A single-stage object detection framework for industrial applications. Preprint at https://arxiv.org/abs/2209.02976 (2022).
Wang, C., Yeh, I. H. & Liao, H. YOLOv9: learning what you want to learn using programmable gradient information. Comput. Vision—ECCV. 15089, 1–21 (2023).
Wang, A. et al. YOLOv10: Real-time end-to-end object detection. Preprint at https://arxiv.org/abs/2503.07465 (2024).
Wang, W., Wang, C., Wei, W., Tang, Y. & Zeng, B. A lightweight object detection algorithm for resource-constrained UAVs via multi-module optimization and channel pruning. King Saud Univ. Comput. Inf. Sci. 37 (339), 1 (2025).
Bae, M. H., Park, S. W., Park, J., Jung, S. H. & Sim, C. B. YOLO-RACE: reassembly and convolutional block attention for enhanced dense object detection. Pattern Anal. Appl. 28 (90), 1 (2025).
Funding
This work was supported in part by the Jiangxi Provincial Department of Water Resources Science & Technology Program Foundation (Grant NO. 202325ZDKT17, 202426ZDKT13).
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Conceptualization, Fang Dong, Binbin Gui and Wenfeng Wang; methodology, Fang Dong, Binbin Gui and Wenfeng Wang; software, Binbin Gui and Wenjie Fan; validation, Binbin Gui and Qihang Liu; formal analysis, Wenfeng Wang and Fang Dong; investigation, Qihang Liu; data curation, Qihang Liu; writing-original draft preparation, Fang Dong, Binbin Gui and Wenfeng Wang; writing-review and editing, Fang Dong, Binbin Gui and Wenfeng Wang; supervision, Wenfeng Wang; project administration, Fang Dong and Wenfeng Wang; funding acquisition, Wenfeng Wang.
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Dong, F., Gui, B., Wang, W. et al. An improved UAV image object detection algorithm combining multi-scale feature fusion and receptive-field attention-based convolution. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34711-y
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DOI: https://doi.org/10.1038/s41598-025-34711-y
