research-article

Nested Double Pyramid Network for Infrared Small Target Detection

Authors:

Zhongdong YangAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 88, Pages 1 - 8

https://doi.org/10.1145/3508546.3508634

Published: 25 February 2022 Publication History

Abstract

In this paper a segmentation based single-frame infrared small target detection (SIRST) network is proposed. This nested double pyramid network (NDPNet) is aiming to solve the conflict between receptive field and target size that limited the existing methods. A feature pyramid network and an atrous spatial pyramid pooling network is nested to form multiscale feature representative and to extract contextual feature at the same time. Experiments on public dataset and a comparison with other state-of-the-art methods demonstrated our better performance on probability of detection (), false-alarm (), and intersection-over-union () and proved the superiority over other networks.

References

[1]

Bai, X., & Zhou, F. (2010). Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recognition, 43(6), 2145-2156.

Digital Library

[2]

Wei, Y., You, X., & Li, H. 2016. Multiscale patch-based contrast measure for small infrared target detection. Pattern Recognition, 58, 216-226.

Digital Library

[3]

Chen, C. P., Li, H., Wei, Y., Xia, T., & Tang, Y. Y. 2013. A local contrast method for small infrared target detection. IEEE transactions on geoscience and remote sensing, 52(1), 574-581.

[4]

Zhu, H., Liu, S., Deng, L., Li, Y., & Xiao, F. 2019. Infrared small target detection via low-rank tensor completion with top-hat regularization. IEEE Transactions on Geoscience and Remote Sensing, 58(2), 1004-1016.

[5]

Nasrabadi, N. M. 2013. Hyperspectral target detection: An overview of current and future challenges. IEEE Signal Processing Magazine, 31(1), 34-44.

[6]

Zhao, J., Tang, Z., Yang, J., & Liu, E. 2011. Infrared small target detection using sparse representation. Journal of systems engineering and electronics, 22(6), 897-904.

[7]

Redmon, J., & Farhadi, A. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

[8]

Ren, S., He, K., Girshick, R., & Sun, J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 91-99.

[9]

Li, Y., Li, S., Du, H., Chen, L., Zhang, D., & Li, Y. 2020. YOLO-ACN: Focusing on Small Target and Occluded Object Detection. IEEE Access, 8, 227288-227303.

[10]

Ryu, J., & Kim, S. 2018. Small infrared target detection by data-driven proposal and deep learning-based classification. In Infrared Technology and Applications XLIV (Vol. 10624, p. 106241J). International Society for Optics and Photonics.

[11]

Bai, J., Zhang, H., & Li, Z. 2018. The generalized detection method for the dim small targets by faster R-CNN integrated with GAN. In 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS) (pp. 1-5). IEEE.

[12]

Wang, H., Zhou, L., & Wang, L. 2019. Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8509-8518).

[13]

Zhao, M., Cheng, L., Yang, X., Feng, P., Liu, L., & Wu, N. 2019. Tbc-net: A real-time detector for infrared small target detection using semantic constraint. arXiv preprint arXiv:2001.05852.

[14]

Shi, M., & Wang, H. 2020. Infrared dim and small target detection based on denoising autoencoder network. Mobile Networks and Applications, 25(4), 1469-1483.

Digital Library

[15]

Dai, Y., Wu, Y., Zhou, F., & Barnard, K. 2021. Asymmetric contextual modulation for infrared small target detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 950-959).

[16]

Dai, Y., Wu, Y., Zhou, F., & Barnard, K. 2020. Single-frame Infrared Small Target Detection Dataset. Retrieved June 6, 2021 from https://github.com/YimianDai/sirst

[17]

Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).

[18]

Huang, L., Dai, S., Huang, T., Huang, X., & Wang, H. 2021. Infrared small target segmentation with multiscale feature representation. Infrared Physics & Technology, 116, 103755.

[19]

Dai, Y., Wu, Y., Zhou, F., & Barnard, K. 2021. Attentional local contrast networks for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing.

[20]

Li, B., Xiao, C., Wang, L., Wang, Y., Lin, Z., Li, M., ... & Guo, Y. 2021. Dense Nested Attention Network for Infrared Small Target Detection. arXiv preprint arXiv:2106.00487.

[21]

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 3-11). Springer, Cham.

[22]

Ronneberger, O., Fischer, P., & Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

[23]

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

[24]

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.

Digital Library

[25]

Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. 2017. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).

[26]

Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

[27]

Yang, M., Yu, K., Zhang, C., Li, Z., & Yang, K. 2018. Denseaspp for semantic segmentation in street scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3684-3692).

[28]

He, K., Zhang, X., Ren, S., & Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[29]

Hu, J., Shen, L., & Sun, G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).

[30]

Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. 2018. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).

Digital Library

[31]

Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Zuo, W.; Hu, Q. ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 16–20 June 2019.

[32]

Qin, X., Wang, Z., Bai, Y., Xie, X., & Jia, H. 2020. FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 11908-11915).

[33]

Máttyus, G., Luo, W., & Urtasun, R. 2017. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3438-3446).

Cited By

Chen GWang WLi X(2023)Designing and learning a lightweight network for infrared small target detection via dilated pyramid and semantic distillationInfrared Physics & Technology10.1016/j.infrared.2023.104671131(104671)Online publication date: Jul-2023
https://doi.org/10.1016/j.infrared.2023.104671

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ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Copyright © 2021 ACM.

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Publication History

Published: 25 February 2022

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ACAI'21

ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Cited By

Chen GWang WLi X(2023)Designing and learning a lightweight network for infrared small target detection via dilated pyramid and semantic distillationInfrared Physics & Technology10.1016/j.infrared.2023.104671131(104671)Online publication date: Jul-2023
https://doi.org/10.1016/j.infrared.2023.104671

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