# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 6 Conclusions  
To address challenges in small-target detection in UAV aerial photography, such as large-scale variation, complex occlusion, and the need for lightweight deployment, we developed FFG-YOLO. This method is a lightweight improvement of YOLOv8n, incorporating FEB, FCB, and GCAB modules. FFG-YOLO successfully performs hierarchical feature extraction and cross-dimensional semantic enhancement, improving its ability to capture fine-grained features and discriminability in complex backgrounds. Additionally, the WDLoss function was integrated to optimize target boundary regression quality, mitigating localization ambiguity caused by traditional loss functions. FFG-YOLO showed superior performance in detection accuracy and inference efficiency with an extremely lightweight design, featuring 800,000 learnable weights, which is a 67% reduction from the baseline. On the VisDrone2019 dataset, FFG-YOLO's small-target detection performance and real-time capabilities enable the efficient operation of lightweight algorithms, overcoming the conventional trade-off between compact design and high performance. This technical approach offers a significant reference for visual perception in low-altitude remote sensing, intelligent inspection, and other similar scenarios. While FFG-YOLO advances multi-scale feature utilization and lightweight architecture, further optimization for target detection and robustness in complex scenes is still necessary. Future studies are required to enhance the model's generalization to tiny targets in cluttered backgrounds with ultra-low computational loads, thereby advancing UAV visual detection technology and improving its practicality and reliability. Conceptualization, T.W. and S.Y. ; methodology, T.W. ; software, S.Y. ; validation, T.W., S.Y. and M.W. ; formal analysis, Y.L. ; investigation, T.W. ; resources, S.Y. ; data curation, T.W. ; writing--original draft preparation, T.W. and S.Y. ; writing--review and editing, T.W. ; visualization, Y.L. ; supervision, Y.L. ; project administration, M.W. ; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data in this study was obtained from [[https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)]([https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)), accessed on 1 July 2025. The authors declare no conflict of interest. List of Acronyms  
* [1] [PERSON]; [PERSON]; [PERSON]; [PERSON] You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27 June 2016; pp. 779-788. * [2] [PERSON]; [PERSON] [PERSON] YOLO9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017. * [3] [PERSON]; [PERSON] [PERSON] YOLOv3: An Incremental Improvement. _arXiv_**2018**, arXiv:1804.02767. [CrossRef]
* [4] [PERSON]; [PERSON]; [PERSON]. YOLOv4: Optimal Speed and Accuracy of Object Detection. _arXiv_**2020**, arXiv:2004.10934. [CrossRef]
* [5] [PERSON]; [PERSON]; [PERSON] A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. _Mach. Learn. Knowl. Extr._**2023**, \(5\), 1680-1716. [CrossRef]
* [6] [PERSON] YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. _Machines_**2023**, _11_, 677. [CrossRef]* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17 June 2023; pp. 7464-7475. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.