FAGNet: Multi-Scale Object Detection Method in Remote Sensing Images by Combining MAFPN and GVR

被引：0
|
作者：
Zheng Z. [1 ]
Lei L. [1 ]
Sun H. [1 ]
Kuang G. [1 ]
机构：
[1] State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science, National University of Defense Technology, Changsha
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2021年 / 33卷 / 06期
关键词：
Attention mechanism; Convolutional neural networks; Feature fusion; Object detection; Remote sensing image;
D O I：
10.3724/SP.J.1089.2021.18608
中图分类号：
学科分类号：
摘要：
Remote sensing images of large scenes are complex, and have the characteristics of many categories of objects, different scales and changeable directions, which lead to the problem of multi-class, multi-scale and multi-oriented of objects in remote sensing images. A remote sensing image object detection method based on multi-scale attention feature pyramid network (MAFPN) and gliding vertex regression (GVR) mechanism is proposed. Firstly, multi-layer feature maps are extracted from backbone network as input of MAFPN, which combines feature fusion and attention mechanism. On the basis of fusing feature maps of multi-scale, channel attention and spatial attention mechanisms are used to suppress noise, enhance effective feature reuse, and improve the network's adaptability to object multi-scale features. The fusion feature map output by MAFPN is input to the region proposal network to generate the regions of interest, and then they will be sent to the classi-fication regression network. The GVR mechanism is used in the object classification regression network and the four vertex offset ratio parameters and rotation factors are added on the basis of predicting the horizontal boxes, which converts the horizontal boxes into a rotating box to reduce the redundant area in the bounding boxes, makes the predicted rotating bounding boxes fit the object more closely. The experimental results on the DOTA public dataset, compared with many classical detection algorithms based on convolutional neural networks, show that the average detection accuracy of the proposed method is significantly improved, which can detect objects of multi-scales and multi-oriented more accurately, and achieve the robust detection of multi-scale ob-jects. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：883 / 894
页数：11
相关论文
共 35 条
  • [1] Xu Chengji, Wang Xiaofeng, Yang Yadong, Attention-YOLO: YOLO detection algorithm that introduces attention mechanism, Computer Engineering and Applications, 55, 6, pp. 13-23, (2019)
  • [2] Ren S Q, He K M, Girshick R, Et al., Faster R-CNN: towards real-time object detection with region proposal networks, Proceedings of the Advances in Neural Information Processing Systems, pp. 91-99, (2015)
  • [3] Dai J F, Li Y, He K M, Et al., R-FCN: object detection via region-based fully convolutional networks, Proceedings of the Advances in Neural Information Processing Systems, pp. 379-387, (2016)
  • [4] He K M, Gkioxari G, Dollar P, Et al., Mask R-CNN, Procee-dings of the IEEE International Conference on Computer Vision, pp. 2961-2969, (2017)
  • [5] Cai Z W, Vasconcelos N., Cascade R-CNN: delving into high quality object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154-6162, (2018)
  • [6] Han X B, Zhong Y F, Zhang L P., An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery, Remote Sensing, 9, 7, (2017)
  • [7] Deng Zhipeng, Sun Hao, Lei Lin, Et al., Object detection in remote sensing imagery with multi-scale deformable convolutional networks, Acta Geodaetica et Cartographica Sinica, 47, 9, pp. 1216-1227, (2018)
  • [8] Li K, Cheng G, Bu S H, Et al., Rotation-insensitive and context-augmented object detection in remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 56, 4, pp. 2337-2348, (2017)
  • [9] Yao Qunli, Hu Xian, Lei Hong, Object detection in remote sensing images using multiscale convolutional neural networks, Acta Optica Sinica, 39, 11, (2019)
  • [10] Tang T Y, Zhou S L, Deng Z P, Et al., Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining, Sensors, 17, 2, (2017)
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