Research on small and medium target detection algorithm for unmanned aerial vehicles based on faster R-CNN

Duojia Zhu; Ping Chen; Jingyi Wei; Shan Gao; Fei Cao

doi:10.1117/12.3026301

27 March 2024 Research on small and medium target detection algorithm for unmanned aerial vehicles based on faster R-CNN

Duojia Zhu, Ping Chen, Jingyi Wei, Shan Gao, Fei Cao

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131053Z (2024) https://doi.org/10.1117/12.3026301
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Drone is widely applied to human production life with the development of drone technology. Therefore, it is very advantageous to carry out research on target detection in UAV images. Using target detection technology, achieve accurate capture of the target, thereby achieving accurate positioning of the target, and achieving the goal of meeting various mission requirements. Unmanned aerial vehicle (UAV) images have the characteristics of large changes in target scale, small overall target size, and are limited by viewing angle, which pose a great challenge to the detection of UAV targets. This thesis mainly focuses on the research of target detection in UAV images. This article proposes an optimization method for small object detection based on Faster R-CNN technology to address the problems of low accuracy in small object detection and missed detection of multi-scale targets in existing drone image object detection algorithms. The improved feature pyramid network structure FPN adopts top-down, bottom-up, and horizontal connection structures. Reduce prediction box bias caused by pooling regions of interest through feature pooling using bilinear interpolation. Finally, the improved algorithm was experimentally validated on the MSCOCO dataset. The faster and Faster R-CNN small object detection algorithm proposed in this article improves the average accuracy of the optimized algorithm from 91.16% to 96.13%. The Faster R-CNN algorithm has improved by 4.97% compared to the original one. The speed of the improved algorithm has been increased to 37FPS, which is approximately doubled.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Duojia Zhu, Ping Chen, Jingyi Wei, Shan Gao, and Fei Cao "Research on small and medium target detection algorithm for unmanned aerial vehicles based on faster R-CNN", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131053Z (27 March 2024); https://doi.org/10.1117/12.3026301

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