Abstract
In order to improve the detection ability of UAVs in complex scenarios in UAV prevention and control tasks, this paper proposes an improved YOLOv8 target detection algorithm based on multiple-detection head and attention enhancement. Firstly, based on the original network of YOLOv8, the feature extraction layer is added, the feature information of the shallow network is combined to retain more details of the UAV, and a new detection head is added to the Head network to improve the detection accuracy of small targets. Secondly, in the deep network containing the feature information of small targets, the attention module CBAM combining the two dimensions of feature channel and feature space is added to improve the detection accuracy and efficiency. Finally, based on the USVEO-20 photoelectric detection sensor, the UAV targets detection verification experiment is carried out. Experimental results show that the improved YOLOv8 model and YOLOv8 identify the same datasets, the detection level and confidence are improved, especially for small target recognition in complex scenes, the effect is better than other models. Compared with YOLOv8, under Det-Fly common datasets, mAP@0.5 andmAP@0.5 and:0.95 of the improved YOLOv8 model are increased by 0.8% and 1.3% respectively. Under the self-made datasets, the mAP@0.5 and mAP@0.5 :0.95 of the improved YOLOv8 model increased by 4.3% and 6.1% respectively. The FPS is greater than 30 frames per second, which meets the real-time requirements of UAV prevention and control.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, S.Z., Wang, H.P., Xu, F.: Target classification using the deep convolutional networks for SAR images. IEEE Trans. Geosci. Remote Sens. (2016)
Chang, X.Y.: Real-time detection and positioning system based on sound array. In: Hangzhou, Zhejiang University (2019)
Wu, H., Xu, J., Li, G.: Status and development of civil UAV detection and countermeasures technology. In: Flying missile, pp. 1–7 (2020)
Liu, Z.H.: Research on a UAV detection algorithm based on deep learning. In: Shanghai, Donghua University (2020)
Zhang, P.F.: Research on UAV intrusion detection in low-altitude airspace. In: Xi ‘an, Chang’ an University (2019)
Zhang, H., Zhang, W.W.: Comparison and analysis of UAV target detection algorithm based on CNN. In: 8th China Index Swing the Control Conference (2020)
Gan, Y.T.: Convolutional neural network in low-altitude airspace UAV detection. In: Chengdu, UESTC (2019)
Zhou, G.B.: Target identification and tracking algorithm in anti-UAV system. In: Qingdao, China University of Petroleum (2018)
Luo, Y., Wang, Z.: An improved resnet algorithm based on CBAM. In: 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA) (2021)
Zhao, Z., Chen, K., Yamane, S.: CBAM-Unet++: easier to find the target with the attention module CBAM. In: 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) (2021)
Tian, Z., Shen, CH., Chen, H.: FCOS: Fully Convolutional One-Stage Object Detection. In: IEEE International Conference on Computer Vision (ICCV) (2019)
Hou, Q.Y., Zhang, L.W., Tan, F.J.: ISTDU-Net: infrared small-target detection U-Net. IEEE Geosci. Remote Sens. Lett. (GRSL) (2022)
Zhu, X.K., Lyu, S.C., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (2021)
Mate, K., Zbigniew, W., Jakub, M., Jacek, N.: Augmentation for small object detection. In: Computer Vision and Pattern Recognition (CVPR) (2019)
Sun, B., Zuo, Z., Wu, P., Tong, X.Z., Guo, R.Z.: Improved SSD target detection method for UAV environment perception. Chinese J. Sci. Instrum. (2020)
Yuan, S.D., Sun, B., Zuo, Z., Huang, H.H., Wu, P., Li, C., Dang, Z.Y., Zhao, Z.Q.: IRSDD-YOLOv5: focusing on the infrared detection of small drones. In: Drones 2023 (2023)
Funding
This work was supported by the National Natural Science Foundation of China under Grants 52101377.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Beijing HIWING Scientific and Technological Information Institute
About this paper
Cite this paper
Li, C., Zuo, Z., Sun, B., Yuan, S., Dang, Z., Di, J. (2024). UAV Target Detection Method Based on Multi-detection Head and Attention in Urban Background. In: Qu, Y., Gu, M., Niu, Y., Fu, W. (eds) Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023). ICAUS 2023. Lecture Notes in Electrical Engineering, vol 1170. Springer, Singapore. https://doi.org/10.1007/978-981-97-1107-9_15
Download citation
DOI: https://doi.org/10.1007/978-981-97-1107-9_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1106-2
Online ISBN: 978-981-97-1107-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)