Joint self-attention and branch sampling for object detection on drone imagery

被引：0
|
作者：
Zhang Y. [1 ,2 ]
Wu C. [1 ]
Liu Y. [1 ]
Zhang T. [1 ]
Zheng Y. [1 ]
机构：
[1] School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang
[2] Hebei Key Laboratory of Electromagnetic Environmental Effects and Information Processing, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2023年 / 31卷 / 18期
关键词：
branch sampling; feature fusion; multi-scale; self attention; UAV image;
D O I：
10.37188/OPE.20233118.2723
中图分类号：
学科分类号：
摘要：
Object detection on drone imagery is widely used in many fields. However， due to the complexity of the image background， the dense small objects and the dramatic scale changes， the existing object detection on drone imagery methods are not accurate enough. In order to solve this problem， we propose an accurate object detection method for drone imagery joint self attention and branch sampling. Firstly， a nested residual structure integrating self attention and convolution is designed to achieve the effective combination of global and local information， which makes the model to focus on the object area and ignore invalid features. Secondly， we design a feature fusion module based on branch sampling to mitigate the loss of object information. Finally， an improved detector for small objects is added to alleviate the problem of sharp scale changes. Furthermore， we propose a feature enhancement module to obtain more discriminative small object features. The experimental results show that the proposed algorithm performs well in various scenarios. Specifically， the mAP50 and mAP of the s model on the VisDrone2019 reached 59.3% and 37.1% respectively， an increase of 5.6% and 5.4% compared with the baseline. The mAP50and mAP on the UAVDT reached 44.1% and 24.9% respectively， an increase of 5.8% and 3.2% compared with the baseline. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：2723 / 2735
页数：12
相关论文
共 30 条
  • [1] QU Y, LI W H., Single-stage rotated object detection network based on anchor transformation［J］, Journal of Jilin University （Engineering and Technology Edition）, 52, 1, pp. 162-173, (2022)
  • [2] LI C, HUANG X Y, WANG K., Small object detection of high-resolution images based on feature fusion and learnable anchor［J］, Acta Electronica Sinica, 50, 7, pp. 1684-1695, (2022)
  • [3] LIU F, HAN X., Adaptive aerial object detection based on multi-scale deep learning［J］, Acta Aeronautica et Astronautica Sinica, 43, 5, pp. 463-474, (2022)
  • [4] DALAL N, TRIGGS B., Histograms of Oriented Gradients for Human Detection［C］, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition （CVPR'05）, pp. 886-893, (2005)
  • [5] LIENHART R, MAYDT J., An Extended Set of Haar-Like Features for Rapid Object Detection［C］, Proceedings of International Conference on Image Processing, (2002)
  • [6] VIOLA P, JONES M., Rapid object detection using a boosted cascade of simple features［C］, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2001)
  • [7] XU D G, WANG L, LI F., Review of typical object detection algorithms for deep learning［J］, Computer Engineering and Applications, 57, 8, pp. 10-25, (2021)
  • [8] ZHANG C G, XIONG B L, KUANG G Y., A survey of ship detection in optical satellite remote sensing images［J］, Chinese Journal of Radio Science, 35, 5, pp. 637-647, (2020)
  • [9] YANG F, FAN H, CHU P, Et al., Clustered object detection in aerial images［C］, 2019 IEEE/CVF International Conference on Computer Vision （ICCV）, pp. 8310-8319, (2019)
  • [10] YU W P, YANG T, CHEN C., Towards resolving the challenge of long-tail distribution in uav images for object detection［C］, 2021 IEEE Winter Conference on Applications of Computer Vision （WACV）, pp. 3257-3266
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