Electronics and Electrical Engineering and Control

Aerial-photography dense small target detection algorithm based on adaptive cooperative attention mechanism

  • Zihao LI ,
  • Zhengping WANG ,
  • Yuntao HE
Expand
  • School of Astronautics,Beijing Institute of Technology,Beijing 100081,China
E-mail: bithyt@bit.edu.cn

Received date: 2022-08-24

  Revised date: 2022-09-05

  Accepted date: 2022-10-20

  Online published: 2022-10-26

Supported by

Aeronautical Science Foundation of China(2020Z005072001)

Abstract

In response to the problem of a large number of targets and a high proportion of small targets in a wide field of view in drone aerial target detection tasks, a drone aerial target detection algorithm ACAM-YOLO based on adaptive collaborative attention mechanism is proposed. In the backbone network and feature enhancement network parts, the Adaptive Co-Attention Module (ACAM) is embedded, which first segments the input features along the channel direction, Then, spatial attention features and channel attention features are separately mined, and finally adaptively weighted into collaborative attention weights to increase the effective utilization of spatial and channel information for input features; To improve detection accuracy while ensuring lightweight of the detection network, the backbone network, feature enhancement network, and detection head are optimized. Firstly, a lightweight backbone network is used to significantly reduce the number of parameters, and then a high-resolution feature enhancement network is used to retain more semantic features and detailed features. Finally, the positioning accuracy is improved by using a large and dense number of anchor boxes in the large-scale detection head. Verified using the public dataset VisDrone2019, compared with the baseline network version 6.0 YOLOv5 object detection algorithm, ACAM-YOLO’s mAP0.5 increased by 11.0%, mAP0.95 increased by 7.8%, and model parameters decreased by 65.5%. The experiment proved that the ACAM-YOLO object detection network has strong practicality for detecting dense small targets in aerial photography.

Cite this article

Zihao LI , Zhengping WANG , Yuntao HE . Aerial-photography dense small target detection algorithm based on adaptive cooperative attention mechanism[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(13) : 327944 -327944 . DOI: 10.7527/S1000-6893.2022.27944

References

1 江波, 屈若锟, 李彦冬, 等. 基于深度学习的无人机航拍目标检测研究综述[J]. 航空学报202142(4):524519
  JIANG B, QU R K, LI Y D, et al. Object detection in UAV imagery based on deep learning: Review[J]. Acta Aeronautica et Astronautica Sinica202142(4):524519 (in Chinese).
2 REN S, HE K, GIRSHICK,et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence201739(6): 1137-1149.
3 LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]∥European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
4 LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence202042(2): 318-327.
5 REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 779-788.
6 REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 6517-6525.
7 REDMON J, FARHADI A. YOLOv3: An incremental improvement[DB/OL]. ArXiv preprint1804.02767, 2018.
8 BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: Optimal speed and accuracy of object detection[DB/OL]. arXiv preprint2004.10934, 2020
9 张艳, 张明路, 吕晓玲, 等. 深度学习小目标检测算法研究综述[J]. 计算机工程与应用202258(15): 1-17.
  ZHANG Y, ZHANG M L, LYU X L, et al. Review of research on small target detection based on deep learning[J]. Computer Engineering and Applications202258(15): 1-17 (in Chinese).
10 李科岑, 王晓强, 林浩, 等. 深度学习中的单阶段小目标检测方法综述[J]. 计算机科学与探索202216(1):41-58.
  LI K C, WANG X Q, LIN H, et al. Survey of one-stage small object detection methods in deep learning[J]. Journal of Frontiers of Computer Science & Technology202216(1):41-58 (in Chinese).
11 曹家乐, 李亚利, 孙汉卿, 等. 基于深度学习的视觉目标检测技术综述[J]. 中国图象图形学报202227(6):1697-1722.
  CAO J L, LI Y L, SUN H Q, et al. A survey on deep learning based visual object detection[J]. Journal of Image and Graphics202227(6):1697-1722 (in Chinese).
12 HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 7132-7141.
13 WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[DB/OL]. ArXiv preprint1807.06521, 2018.
14 HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]∥ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 13708-13717.
15 LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]∥ 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 936-944.
16 LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 8759-8768.
17 TAN M X, PANG R M, LE Q V. EfficientDet: Scalable and efficient object detection[C]∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 10778-10787.
18 CHEN Y K, ZHANG P Z, LI Z, et al. Stitcher: Feedback-driven data provider for object detection[DB/OL]. arXiv preprint2004.12432, 2020.
19 YUN S, HAN D, CHUN S, et al. CutMix: Regularization strategy to train strong classifiers with localizable features[C]∥ 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2020: 6022-6031.
20 KISANTAL M, WOJNA Z, MURAWSKI J, et al. Augmentation for small object detection[C]∥ 9th International Conference on Advances in Computing and Information Technology (ACITY 2019), 2019.
21 DU D W, ZHU P F, WEN L Y, et al. VisDrone-DET2019: The vision meets drone object detection in image challenge results[C]∥ 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Piscataway: IEEE Press, 2020: 213-226.
22 冒国韬, 邓天民, 于楠晶. 基于多尺度分割注意力的无人机航拍图像目标检测算法[J].航空学报202344(5): 326738.
  MAO G T, DENG T M, YU N J. Object detection in UAV images based on multi-scale split attetion[J]. Acta Aeronautica et Astronautica Sinica202344(5): 326738 (in Chinese).
Outlines

/