Abstract
In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource.
B. Li, X. Ying, R. Li, Y. Liu, Y. Shi, M. Li, X. Zhang, M. Hu, C. Wu, Y. Zhang, H. Wei, D. Tang, J. Zhao, L. Jin, C. Xiao, Q. Ling, Z. Lin, W. Sheng—The ICPR-2024 LimitIRSTD challenge organizers and share the equal contribution, while the other authors participated in this challenge.
ICPR 2024 webpage: https://icpr2024.org/.
Challenge webpage: https://limitirstd.github.io/.
Leaderboard Track1: https://bohrium.dp.tech/competitions/8821868197?tab=introduce.
Leaderboard Track2: https://bohrium.dp.tech/competitions/9012970343?tab=introduce.
BasicIRSTD toolbox: https://github.com/XinyiYing/BasicIRSTD.
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References
Berman, M., Triki, A.R., Blaschko, M.B.: The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4413–4421 (2018)
Beyer, L., Zhai, X., Royer, A., Markeeva, L., Anil, R., Kolesnikov, A.: Knowledge distillation: a good teacher is patient and consistent. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10925–10934 (2022)
Bochkovskiy, A.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Chen, W.T., Vong, Y.J., Kuo, S.Y., Ma, S., Wang, J.: RobustSAM: segment anything robustly on degraded images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4081–4091, June 2024
Dai, Y., Li, X., Zhou, F., Qian, Y., Chen, Y., Yang, J.: One-stage cascade refinement networks for infrared small target detection. IEEE Trans. Geosci. Remote Sens. 61, 1–17 (2023)
Dinh, B.D., Nguyen, T.T., Tran, T.T., Pham, V.T.: 1m parameters are enough? A lightweight CNN-based model for medical image segmentation. In: 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1279–1284. IEEE (2023)
Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918–2928 (2021)
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., Wilson, A.G.: Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018)
Jiang, N., et al.: Anti-UAV: a large-scale benchmark for vision-based UAV tracking. IEEE Trans. Multimedia 25, 486–500 (2021)
Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kou, R., et al.: MCGC: a multi-scale chain growth clustering algorithm for generating infrared small target mask under single-point supervision. IEEE Trans. Geosci. Remote Sens. (2024)
Li, B., et al.: Mixed-precision network quantization for infrared small target segmentation. IEEE Trans. Geosci. Remote Sens. (2024)
Li, B., et al.: Monte Carlo linear clustering with single-point supervision is enough for infrared small target detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1009–1019, October 2023
Li, B., et al.: Dense nested attention network for infrared small target detection. IEEE Trans. Image Process. 32, 1745–1758 (2022)
Li, R., et al.: Direction-coded temporal U-shape module for multiframe infrared small target detection. IEEE Trans. Neural Netw. Learn. Syst. (2023)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Lin, Z., et al.: Light-weight infrared small target detection combining cross-scale feature fusion with bottleneck attention module. J. Infrared Millim. Waves 41(6), 1102–1112 (2022)
Liu, T., Yang, J., Li, B., Wang, Y., An, W.: Infrared small target detection via nonconvex tensor tucker decomposition with factor prior. IEEE Trans. Geosci. Remote Sens. (2023)
Liu, T., et al.: Nonconvex tensor low-rank approximation for infrared small target detection. IEEE Trans. Geosci. Remote Sens. 60, 1–18 (2021)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
Qin, Y., Bruzzone, L., Gao, C., Li, B.: Infrared small target detection based on facet kernel and random walker. IEEE Trans. Geosci. Remote Sens. 57(9), 7104–7118 (2019)
Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part I. LNCS, vol. 11070, pp. 421–429. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_48
Sun, H., Bai, J., Yang, F., Bai, X.: Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset irdst. IEEE Trans. Geosci. Remote Sens. 61, 1–13 (2023)
Wang, H., Zhou, L., Wang, L.: 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 (2019)
Wu, J., et al.: Single-point supervised high-resolution dynamic network for infrared small target detection. arXiv preprint arXiv:2408.01976 (2024)
Wu, T., et al.: MTU-Net: multilevel TransUNet for space-based infrared tiny ship detection. IEEE Trans. Geosci. Remote Sens. 61, 1–15 (2023)
Wu, X., Hong, D., Chanussot, J.: UIU-Net: U-Net in U-Net for infrared small object detection. IEEE Trans. Image Process. 32, 364–376 (2022)
Ying, X., et al.: Mapping degeneration meets label evolution: learning infrared small target detection with single point supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15528–15538 (2023)
Yu, C., et al.: Infrared small target detection based on multiscale local contrast learning networks. Infrared Phys. Technol. 123, 104107 (2022)
Yu, C., et al.: Pay attention to local contrast learning networks for infrared small target detection. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
Yu, C., Liu, Y., Zhao, J., Shi, Z.: LR-Net: a lightweight and robust network for infrared small target detection. arXiv preprint arXiv:2408.02780 (2024)
Yuan, S., et al.: Beyond full label: single-point prompt for infrared small target label generation. arXiv preprint arXiv:2408.08191 (2024)
Yuan, S., Qin, H., Yan, X., Akhtar, N., Mian, A.: SCTransNet: spatial-channel cross transformer network for infrared small target detection. IEEE Trans. Geosci. Remote Sens. (2024)
Zhang, M., Zhang, R., Yang, Y., Bai, H., Zhang, J., Guo, J.: ISNet: shape matters for infrared small target detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 877–886 (2022)
Zhao, J., Shi, Z., Yu, C., Liu, Y.: Infrared small target detection based on adjustable sensitivity strategy and multi-scale fusion. arXiv preprint arXiv:2407.20090 (2024)
Zhao, J., Shi, Z., Yu, C., Liu, Y.: Refined infrared small target detection scheme with single-point supervision. arXiv preprint arXiv:2408.02773 (2024)
Zhao, J., Yu, C., Shi, Z., Liu, Y., Zhang, Y.: Gradient-guided learning network for infrared small target detection. IEEE Geosci. Remote Sens. Lett. (2023)
Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (Nos. 62401591, 62401589).
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Li, B. et al. (2025). ICPR 2024 Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. Competitions. ICPR 2024. Lecture Notes in Computer Science, vol 15334. Springer, Cham. https://doi.org/10.1007/978-3-031-80139-6_5
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