[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]

Journal of Projectiles, Rockets, Missiles and Guidance >

2022 , Vol. 42 >Issue 5: 102 - 107

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2022.05.019

Missile Borne Fusion Image Visual Explanations for Deep Convolutional Networks

  • XUE Song 1, 2 ,
  • QIAN Lizhi 1, 2 ,
  • YANG Chuandong 3
Expand
  • 1 Department of Weapons Engineering,Army Academy of Artillery and Air Defense,Hefei 230031,China
  • 2 High Overload Ammunition Guidance Control and Information Perception Laboratory,Army Academy of Artillery and Air Defense,Hefei 230031,China
  • 3 Postgraduate Team,Army Academy of Artillery and Air Defense,Hefei 230031,China

Received date: 2022-04-28

  Online published: 2025-01-16

Fold

Abstract

In recent years, the decision-making process of convolutional neural networks has attracted more and more attention. Its internal operating mechanism prompts researchers to conduct in-depth research, and forms a visual interpretation theory method based on saliency map. The paper presents a deep convolutional network vision explanations method for missile borne fusion images. In this method, the gradient map of neural network is reconstructed by the “increase of confidence”, and the saliency map is obtained by combining the weight parameters. The experimental results show that compared with the classical visual interpretation methods, the paper method has good subjective visual effect, and achieves the best in the two indicators of average drop and average increase, and has more accurate positioning ability.

Key words： fusion image; saliency map; convolutional neural networks; computer vision

Cite this article

XUE Song , QIAN Lizhi , YANG Chuandong . Missile Borne Fusion Image Visual Explanations for Deep Convolutional Networks[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022 , 42(5) : 102 -107 . DOI: 10.15892/j.cnki.djzdxb.2022.05.019

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks. advances[J]. Neural Information Processing Systems, 2012, 25: 1097-1105.

[2]
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.

[3]
HUANG G, LIU Z L, MAATEN V D, et al. Densely connected convolutional networks[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 2261-2269.

[4]
REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 2767-2773.

[5]
HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transcations on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.

[6]
SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

DOI PMID

[7]
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 2020, 128(2): 336-359.

[8]
ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]// ECCV. Proceedings of the European conference on computer vision. Berlin:Springer, 2014: 818-833.

[9]
ZHOU B L, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 2921-2929.

[10]
CHATTOPADHAY A, SARKAR A, HOWLADER P, et al. Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks[C]// IEEE. Proceedings of the Winter Conference on Applications of Computer Vision. New York: IEEE, 2018: 839-847.

[11]
DESAI S, RAMASWAMY H G. Ablation-CAM: visual explanations for deep convolutional network via gradient-free localization[C]// IEEE. Proceedings of the Winter Conference on Applications of Computer Vision. New York: IEEE, 2020: 983-991.

[12]
FU R G, HU Q Y, DONG, X H, et al. Axiom-based Grad-CAM: towards accurate visualization and explanation of CNNs[EB/OL]. (2020-08-05) [2022-04-15]. https://arxiv.org/abs/2008.02312.

[13]
薛松, 钱立志, 张航, 等. 多源末制导弹载融合图像目标检测研究进展[J]. 弹箭与制导学报, 2021, 41(3):67-75.

[14]
霍星, 邹韵, 陈影, 等. 双尺度分解和显著性分析相结合的红外与可见光图像融合[J]. 中国图象图形学报, 2021, 26(12):2813-2825.

[15]
SURAJ S, FLEURET F. Full-gradient representation for neural network visualization[EB/OL]. (2019-05-02) [2022-04-15]. https://arxiv.org/abs/1905.00780.

[16]
ANG H F, WANG Z F, DU M N, et al. Score-CAM: score-weighted visual explanations for convolutional neural networks[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. New York: IEEE, 2020: 111-119.

[17]
VITALI P, ABIR D, KATE S. RISE: randomized input sampling for explanation of black-box models[EB/OL]. (2018-06-19)[2022-04-15]. https://arxiv.org/abs/1806.07421.

[18]
ZHANG J M, ZHE L, BRANDT J, et al. Top-down neural attention by excitation backprop[C]// ECCV. Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2016: 543-559.

Options
Outlines

/

〈 〉

[an error occurred while processing this directive]