Abstract
Infrared small target detection is a critical topic and research focus in target detection. Compared to visible light and radar detection, infrared imaging-based detection can effectively avoid illumination limitations and potential exposure risks. However, detecting small infrared targets with complex backgrounds and significant noise is challenging, and existing algorithms often have low detection rates, high false alarm rates, long calculation times, and unsatisfactory performance. To address these issues, we proposes an infrared small target detection algorithm based on sparse representation. The algorithm enhances target sparsity through multi-scale contrast saliency mapping and global gray value fusion, leveraging the low rank of the background. We evaluate the proposed method on SIRST dataset and compare its performance with traditional and recent algorithms. The results demonstrate the superiority of our algorithm in terms of detection rate, false alarm rate, and calculation time.
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References
Dai, Y., Wu, Y., Song, Y., Guo, J.: Non-negative infrared patch-image model: robust target-background separation via partial sum minimization of singular values. Infrared Phys. Technol. 81, 182–194 (2017)
Dai, Y., Wu, Y., Zhou, F., Barnard, K.: Asymmetric contextual modulation for infrared small target detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 950–959 (2021)
Deshpande, S.D., Er, M.H., Venkateswarlu, R., Chan, P.: Max-mean and max-median filters for detection of small targets. In: Signal and Data Processing of Small Targets 1999, vol. 3809, pp. 74–83. SPIE (1999)
Gao, C., Meng, D., Yang, Y., Wang, Y., Zhou, X., Hauptmann, A.G.: Infrared patch-image model for small target detection in a single image. IEEE Trans. Image Process. 22(12), 4996–5009 (2013)
Han, J., et al.: Infrared small target detection based on the weighted strengthened local contrast measure. IEEE Geosci. Remote Sens. Lett. 18(9), 1670–1674 (2020)
Jeong, J., Park, H., Kwak, N.: Enhancement of SSD by concatenating feature maps for object detection. arXiv preprint arXiv:1705.09587 (2017)
Jinhui, H., et al.: Infrared dim and small target detection: a review. Infrared Laser Eng. 51(1), 20210393–1 (2022)
Kong, T., Yao, A., Chen, Y., Sun, F.: HyperNet: towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 845–853 (2016)
Li, B., et al.: Dense nested attention network for infrared small target detection. IEEE Trans. Image Process. 32, 1745–1758 (2022)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Wang, X., Peng, Z., Kong, D., Zhang, P., He, Y.: Infrared dim target detection based on total variation regularization and principal component pursuit. Image Vis. Comput. 63, 1–9 (2017)
Xia, C., Chen, S., Zhang, X., Chen, Z., Pan, Z.: Infrared small target detection via dynamic image structure evolution. IEEE Trans. Geosci. Remote Sens. 60(3), 1–18 (2022)
Xiong, B., Huang, X., Wang, M.: Local gradient field feature contrast measure for infrared small target detection. IEEE Geosci. Remote Sens. Lett. 18(3), 553–557 (2020)
Zhang, L., Peng, L., Zhang, T., Cao, S., Peng, Z.: Infrared small target detection via non-convex rank approximation minimization joint l 2, 1 norm. Remote Sens. 10(11), 1821 (2018)
Zhang, L., Peng, Z.: Infrared small target detection based on partial sum of the tensor nuclear norm. Remote Sens. 11(4), 382 (2019)
Zhang, T., Peng, Z., Wu, H., He, Y., Li, C., Yang, C.: Infrared small target detection via self-regularized weighted sparse model. Neurocomputing 420, 124–148 (2021)
Zhang, T., Wu, H., Liu, Y., Peng, L., Yang, C., Peng, Z.: Infrared small target detection based on non-convex optimization with Lp-norm constraint. Remote Sens. 11(5), 559 (2019)
Zhao, M., Li, W., Li, L., Hu, J., Ma, P., Tao, R.: Single-frame infrared small-target detection: a survey. IEEE Geosci. Remote Sens. Mag. 10(2), 87–119 (2022)
Zuo, W., Lin, Z.: A generalized accelerated proximal gradient approach for total-variation-based image restoration. IEEE Trans. Image Process. 20(10), 2748–2759 (2011)
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This work was supported by the Fundamental Research Funds for the Central Universities.
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Yang, D., Zhang, H., Xie, F., Jiang, Z. (2023). Infrared Small Target Detection Based on Prior Weighed Sparse Decomposition. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_11
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DOI: https://doi.org/10.1007/978-981-99-7549-5_11
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