# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 6 Conclusions  
After thoroughly considering the challenges and hardware limitations of unmanned systems, the USD-YOLO algorithm is proposed to improve detection precision in the perception process of unmanned systems. Due to the hardware and software constraints of unmanned systems, it is necessary to balance detection precision and speed. The single-stage detection algorithm YOLOv8s was selected as the baseline, and three improved modules--APE, SCConv, and CGAM--were integrated into the post-processing, backbone, and neck sections, respectively, to enhance detection precision.  
Figure 10: Comparisons of real-time detection results on the real UGV. (**a**) Detected by YOLOv8. (**b**) Detected by YOLOv10. (**c**) Detected by USD-YOLO.  
Experiments were conducted on three widely used datasets in unmanned systems research, each containing a large number of small objects. Ablation and comparison experiments on the Citypersons dataset demonstrated that, compared with the baseline algorithm, USD-YOLO significantly improved the Recall, mAP50, and mAP50-95 metrics by 2.3%, 5.9%, and 8.5%, respectively, while maintaining a high FPS. Generalization experiments on the FloW-Img and DOTA-v1.0 datasets showed that USD-YOLO also achieved the highest detection precision and excellent real-time performance. Additionally, visual comparisons of detection results on these two datasets further highlighted the superiority of the USD-YOLO algorithm. These experimental results prove that, compared with SOTA models, USD-YOLO offers high detection precision, fast speed, and strong generalization capabilities. This holds significant importance for small object perception research in unmanned systems such as UGVs, USVs, and UAVs.  
In future research, efforts will continue to explore ways to improve the detection precision and speed of small objects in unmanned systems under more complex scenarios, such as environments with varying lighting conditions or foggy weather. Additionally, the study will focus on balancing the detail improvements with the high resource consumption of high-resolution cameras.  
Methodology, H.D.; software, S.Z.; validation, X.W.; data curation, T.H.; writing-original draft, H.D.; writing--review and editing, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQN25E080011, in part by the Ningbo Natural Science Foundation under Grant 2024J440, in part by the National Natural Science Foundation of China under Grants 62203434, and in part by the Liaoning Provincial Natural Science Foundation Program of China under Grant 2023-MS-032.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. Citypersons dataset can be found here: [[https://github.com/cvgroup-njust/CityPersons](https://github.com/cvgroup-njust/CityPersons)]([https://github.com/cvgroup-njust/CityPersons](https://github.com/cvgroup-njust/CityPersons)) (accessed on 7 March 2025). FloW-Img dataset can be found here:[[https://github.com/ORCA-Uboat/FloW-Dataset](https://github.com/ORCA-Uboat/FloW-Dataset)]([https://github.com/ORCA-Uboat/FloW-Dataset](https://github.com/ORCA-Uboat/FloW-Dataset)) (accessed on 7 March 2025). DOTA-v1.0 dataset can be found here: [[https://captain-whu.github.io/DOTA/index.html](https://captain-whu.github.io/DOTA/index.html)]([https://captain-whu.github.io/DOTA/index.html](https://captain-whu.github.io/DOTA/index.html)) (accessed on 7 March 2025).  
The authors would like to thank [PERSON] from the Shenyang Institute of Automation, Chinese Academy of Sciences, and [PERSON] from the College of Mechanical Engineering and Automation, Northeastern University, for their valuable support and assistance with resources and funding for this work.  
The authors declare no conflicts of interest.