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Academic Journal of Computing & Information Science, 2025, 8(7); doi: 10.25236/AJCIS.2025.080709.

Improvement and Implementation of UAV Target Detection Algorithm Based on YOLOv10

Author(s)

Chang Cai1, Yanwen Wang1, Pengfei Ma1, Bo Fang1, Minghao Cao1

Corresponding Author:
Chang Cai
Affiliation(s)

1School of Electronic Information, Xijing University, Xi’an, China

Abstract

With the wide application of drones in agriculture, surveying and mapping, power inspection, security and other fields, its safety management problems are becoming increasingly prominent. Traditional UAV detection methods such as radar, infrared, acoustic and radio frequency detection have limitations such as low accuracy, high cost or poor anti-interference in complex environments. Visual inspection technology has become a research hotspot due to its high resolution and good target recognition ability. Focusing on the UAV target detection task, based on the YOLOv10 network structure, this paper proposes an improved detection algorithm to improve the detection accuracy and small target perception ability. Experimental results show that the improved algorithm can significantly improve the recognition effect of multi-scale UAV targets while maintaining the detection speed, and has good engineering application value.

Keywords

UAV Detection, Deep Learning, Target Detection

Cite This Paper

Chang Cai, Yanwen Wang, Pengfei Ma, Bo Fang, Minghao Cao. Improvement and Implementation of UAV Target Detection Algorithm Based on YOLOv10. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 7: 72-78. https://doi.org/10.25236/AJCIS.2025.080709.

References

[1] Kim J K , Shin Y J , Kim J H ,et al .Faraway Small Drone Detection based on Deep Learning[J]. International journal of computer science and network security, 2020, 20(1).

[2] Saqib M , Sharma N , Khan S D ,et al. A Study on Detecting Drones Using Deep Convolutional Neural Networks[C]//AVSS 2017.IEEE Computer Society, 2017.DOI:10.1109/AVSS.2017.8078541.

[3] Xu X , Sun Y , Ding L ,et al. A Novel Infrared Small Target Detection Algorithm Based on Deep Learning[C]//ICAIP 2020: 2020 4th International Conference on Advances in Image Pr-ocessing.2020. DOI:10.1145/3441250.3441258.

[4] Al-Lqubaydhi N , Alenezi A , Alanazi T ,et al. Deep learning for unmanned aerial vehicles detection: A review[J].Computer Science Review, 2024, 51.DOI:10.1016/j.cosrev.2023.100614.

[5] Lenhard T R , Weinmann A , Jger S ,et al. YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection[J].IEEE, 2024.DOI:10.1109/ICIP51287.2024.10647355.

[6] Ghazlane Y , Ahmed E H A , Hicham M . Real-time lightweight drone detection model: Fin-e-grained Identification of four types of drones based on an improved Yolov7 model[J]. Neurocomputing, 2024, 596(000):15.DOI:10.1016/j.neucom.2024.127941.

[7] Jiang R , Zhou Y , Peng Y . A Review on Intrusion Drone Target Detection Based on Deep Learning[J]. IEEE, 2021.DOI:10.1109/IMCEC51613.2021.9482092.

[8] Mei J , Zhu W . BGF-YOLOv10: Small Object Detection Algorithm from Unmanned Aerial Vehicle Perspective Based on Improved YOLOv10[J].Sensors (14248220), 2024, 24(21).DOI:10. 3390/s24216911.

[9] Wang H , Zhang Y , Zhu C . DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s[J].Computers, Materials & Continua, 2025, 83(2).DOI:10.32604/cmc.2025. 061363.

[10] Bo C , Wei Y , Wang X ,et al. Vision-Based Anti-UAV Detection Based on YOLOv7-GS in Complex Backgrounds[J]. Drones (2504-446X), 2024, 8(7).DOI:10.3390/drones8070331.