STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets
Abstract
:1. Introduction
1.1. Related Works
1.1.1. Single-Frame Infrared Dim and Small Target Detection
1.1.2. Multiframe Infrared Dim and Small Target Detection
1.2. Motivation
- (1)
- The spatial and temporal dimensions were integrated into a multiframe IRDST detection network. The salient airspace features and time-domain motion characteristics of the target were incorporated into a unified network architecture based on 3D convolution, which achieved multiframe IRDST detection via an end-to-end network.
- (2)
- According to the consistency of the short-term moving direction of the IRDST, its multiscale three-dimensional motion features with fixed weights were convolved to synchronously enhance the temporal and spatial significance of the target and inhibit random clutter and noise, which improved the detectability of IRDSTs with low SCRs.
- (3)
- The spatiotemporal characteristics of five parallel and independent optimization strategies were incorporated into the designed spatiotemporal feature fusion-based processing module, which successfully mapped spatiotemporal tensors to target multiframe probability maps.
- (4)
- Compared with the current methods, the proposed strategy exhibited better detection performance, especially for IRDSTs with low SCRs.
2. Methods
2.1. Overall Architecture
2.2. Spatial Saliency Feature Generation Module
2.3. Motion Feature Extraction Module
2.4. Spatiotemporal Feature Fusion Module
2.5. Loss Function
2.6. Result-Level Fusion in the Implementation
3. Experiments and Results
3.1. Dataset
3.2. Performance Evaluation Indices
3.3. Network Training
3.4. Ablation Study
- (1)
- The AUC achieved on the test set by the network with motion feature extraction was better than that of the network without motion feature extraction. Motion feature extraction can significantly improve the ability to detect IRDSTs.
- (2)
- Fusion processing was superior to non-fusion processing. After conducting a comparison, the AUC of the maximum value fusion method was the highest.
3.5. Visual Analysis of Feature Maps
3.6. Comparison Experiments
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Methods | Motion Feature Extraction | Fusion Method |
---|---|---|---|
1 | STIDNet-NMFE-Sum | ✘ | SUM |
2 | STIDNet-NMFE-Max | ✘ | MAX |
3 | STIDNet-NMFE-None | ✘ | ✘ |
4 | STIDNet-MFE-Sum | ✓ | SUM |
5 | STIDNet-MFE-Max | ✓ | MAX |
6 | STIDNet-MFE-None | ✓ | ✘ |
No. | STIDNet-NMFE-Sum | STIDNet-NMFE-Max | STIDNet-NMFE-None | STIDNet-MFE-Sum | STIDNet-MFE-Max | STIDNet-MFE-None |
---|---|---|---|---|---|---|
Seq. 1 | 0.99906 | 0.99916 | 0.99907 | 0.99921 | 0.99921 | 0.99916 |
Seq. 2 | 0.95076 | 0.96522 | 0.92476 | 0.99832 | 0.99904 | 0.9945 |
Seq. 3 | 0.99909 | 0.99906 | 0.99889 | 0.99912 | 0.99912 | 0.99913 |
Seq. 4 | 0.99878 | 0.99876 | 0.99866 | 0.99877 | 0.99876 | 0.99878 |
Seq. 5 | 0.99885 | 0.99885 | 0.99885 | 0.99885 | 0.99885 | 0.99885 |
Seq. 6 | 0.99849 | 0.99881 | 0.99871 | 0.99889 | 0.99888 | 0.99889 |
Seq. 7 | 0.99931 | 0.9993 | 0.99931 | 0.99931 | 0.9993 | 0.99931 |
Seq. 8 | 0.9653 | 0.9695 | 0.94587 | 0.98699 | 0.98974 | 0.9888 |
Mean | 0.988705 | 0.9910825 | 0.983015 | 0.9974325 | 0.9978625 | 0.9971775 |
Methods | Publication | Parameter Settings | Params (M) | FPS |
---|---|---|---|---|
MPCM [10] | Pattern Recognit. 2016 | L = 3; Window size = [3, 5, 7, 9] | - | 36.331 |
HBMLCM [35] | IEEE Geosci. Remote Sens. Lett. 2018 | External window size = 15 × 15; target size = [3, 5, 7, 9] | - | 126.098 |
WSLCM [36] | IEEE Geosci. Remote Sens. Lett. 2021 | Gauss_krl = [1, 2, 1; 2, 4, 2; 1, 2, 1]./16; Scs = [5, 7, 9, 11] | - | 0.801 |
RLCM [11] | IEEE Geosci. Remote. Sens. Lett. 2018 | Scale = 3; k1 = [2, 5, 9]; k2 = [4, 9, 16] | - | 1.024 |
TLLCM [13] | IEEE Geosci. Remote. Sens. Lett. 2019 | GS = [1/16, 1/8, 1/16; 1/8, 1/4, 1/8; 1/16, 1/8, 1/16] | - | 1.533 |
NIPPS [15] | Infr. Phys. Technol. 2017 | PatchSize = 50; SlideStep = 10; LambdaL = 2; RatioN = 0.005 | - | 0.249 |
RIPT [16] | IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2017 | PatchSize = 30; SlideStep = 10; LambdaL = 0.7; MuCoef = 5; h = 1 | - | 1.238 |
WLDM [37] | IEEE Trans. Geosci. Remote Sens. 2016 | L = 9 | - | 0.389 |
FKRW [38] | IEEE Trans. Geosci. Remote Sens. 2019 | L = [−4, −1, 0, −1, −4; −1, 2, 3, 2, −1; 0, 3, 4, 3, 0;−1, 2, 3, 2, −1; −4, −1, 0, −1, −4] | - | 9.034 |
MGRG [39] | IEEE Geosci. Remote. Sens. Lett. 2019 | numSeeds = 20; tarRate = 0.01 × 0.15 | - | 1.422 |
STLCF [25] | Computers and Electrical Engineering. 2018 | tspan_rng = 2; swind_rng = 7 | - | 2.749 |
ISTDUNet [33] | IEEE Trans. Geosci. Remote Sens. Lett. 2022 | hyperparameter of channel = 2 | 2.761 | 23.027 |
RISTDNet [17] | IEEE Trans. Geosci. Remote Sens. Lett. 2022 | - | 0.763 | 4.723 |
DNANet [19] | IEEE Trans. Image Process. 2023 | - | 1.134 | 20.747 |
DTUMNet [32] | IEEE Trans. on Neural Networks and Learning Systems. 2023 | Res-Unet | 0.298 | 9.494 |
STIDNet | - | - | 4.874 | 5.945 |
Seq. | WSLCM | RLCM | TLLCM | NIPPS | RIPT | STLCF | ISTDUNet | RISTDNet | DNANet | DTUMNet | STIDNet | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
S C R > 3 | 1 | 0.9987 | 0.9972 | 0.9979 | 0.9987 | 0.9087 | 0.9833 | 0.9987 | 0.9987 | 0.9727 | 0.9987 | 0.99865 |
2 | 0.9689 | 0.9713 | 0.9990 | 0.8690 | 0.8241 | 0.9926 | 0.9990 | 0.9972 | 0.9890 | 0.9990 | 0.99901 | |
3 | 0.9989 | 0.9989 | 0.9989 | 0.9939 | 0.8840 | 0.9989 | 0.9989 | 0.9989 | 0.9775 | 0.9989 | 0.9989 | |
4 | 0.9989 | 0.9989 | 0.9989 | 0.9989 | 0.9989 | 0.9989 | 0.9989 | 0.9989 | 0.9984 | 0.9989 | 0.99885 | |
5 | 0.9981 | 0.9933 | 0.9965 | 0.9982 | 0.6837 | 0.9844 | 0.9982 | 0.9982 | 0.9982 | 0.9982 | 0.9982 | |
6 | 0.9987 | 0.9930 | 0.9985 | 0.9787 | 0.7290 | 0.9987 | 0.9987 | 0.9987 | 0.5054 | 0.9987 | 0.99866 | |
7 | 0.9990 | 0.9936 | 0.9990 | 0.9990 | 0.9990 | 0.9990 | 0.9990 | 0.9990 | 0.9990 | 0.9990 | 0.99903 | |
8 | 0.9501 | 0.9577 | 0.9957 | 0.5691 | 0.4993 | 0.9823 | 0.9954 | 0.9641 | 0.7676 | 0.9987 | 0.99873 | |
9 | 0.9881 | 0.9959 | 0.9981 | 0.9426 | 0.4994 | 0.9871 | 0.9987 | 0.9983 | 0.9362 | 0.9989 | 0.99889 | |
10 | 0.9982 | 0.9959 | 0.9983 | 0.9979 | 0.5940 | 0.9952 | 0.9984 | 0.9985 | 0.4848 | 0.9985 | 0.99845 | |
11 | 0.9780 | 0.9249 | 0.9983 | 0.8885 | 0.6490 | 0.9965 | 0.9985 | 0.9978 | 0.9438 | 0.9985 | 0.99853 | |
12 | 0.9990 | 0.9989 | 0.9990 | 0.9989 | 0.9989 | 0.9983 | 0.9990 | 0.9990 | 0.9989 | 0.9989 | 0.99895 | |
Mean | 0.98955 | 0.98496 | 0.99818 | 0.93612 | 0.77233 | 0.99293 | 0.99845 | 0.99561 | 0.88096 | 0.99874 | 0.99874 | |
S C R < 3 | 1 | 0.51761 | 0.50963 | 0.87721 | 0.72535 | 0.51833 | 0.97573 | 0.95366 | 0.7933 | 0.78389 | 0.98842 | 0.99921 |
2 | 0.5213 | 0.7861 | 0.9832 | 0.4996 | 0.4993 | 0.7348 | 0.9933 | 0.5819 | 0.4579 | 0.7737 | 0.99904 | |
3 | 0.5505 | 0.5343 | 0.8537 | 0.5589 | 0.5509 | 0.8941 | 0.9962 | 0.9319 | 0.6491 | 0.9834 | 0.99912 | |
4 | 0.5436 | 0.4658 | 0.8897 | 0.4994 | 0.4989 | 0.6392 | 0.9900 | 0.8321 | 0.5795 | 0.9053 | 0.99876 | |
5 | 0.5274 | 0.5913 | 0.9778 | 0.6923 | 0.4981 | 0.7893 | 0.9979 | 0.9772 | 0.8919 | 0.9989 | 0.99885 | |
6 | 0.5042 | 0.4859 | 0.9244 | 0.5091 | 0.4993 | 0.8581 | 0.9833 | 0.9037 | 0.6659 | 0.9975 | 0.99888 | |
7 | 0.4992 | 0.4780 | 0.5788 | 0.7351 | 0.5656 | 0.9789 | 0.9993 | 0.8576 | 0.7410 | 0.8156 | 0.9993 | |
8 | 0.4989 | 0.7642 | 0.9202 | 0.7658 | 0.6277 | 0.9287 | 0.9217 | 0.7248 | 0.8154 | 0.9285 | 0.98974 | |
Mean | 0.52034 | 0.57690 | 0.87563 | 0.62319 | 0.53227 | 0.84985 | 0.97942 | 0.82531 | 0.69807 | 0.92392 | 0.99786 |
Seq. | WSLCM | RLCM | TLLCM | NIPPS | RIPT | STLCF | ISTDUNet | RISTDNet | DNANet | DTUMNet | STIDNet | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
S C R > 3 | 1 | 38.6 | 5.6 | 9.14 | 278.3 | 32.5 | 2.1 | 1358.4 | 278.3 | 5.7 | 1365.3 | 2251.7 |
2 | 988.5 | 7.2 | 9.59 | 524.6 | 1860.2 | 2.9 | 240.3 | 47.6 | 1.3 | 588.3 | 1711.0 | |
3 | 2109.6 | 15.4 | 64.0 | 257.9 | 1046.1 | 5.9 | 746.3 | 204.1 | 7.1 | 795.7 | 1848.9 | |
4 | 20.3 | 3.0 | 6.4 | 11.2 | 8.1 | 2.3 | 52.4 | 8.4 | 2.7 | 1300.9 | 1557.6 | |
5 | 14.2 | 1.4 | 2.6 | 15.2 | 19.5 | 1.3 | 13.7 | 11.5 | 0.9 | 529.6 | 935.2 | |
6 | 18.5 | 3.4 | 6.8 | 11.0 | 24.3 | 2.2 | 43.5 | 39.4 | 1.6 | 534.5 | 1484.5 | |
7 | 23.1 | 1.3 | 3.4 | 6.4 | 9.8 | 1.1 | 8.1 | 14.9 | 0.3 | 196.0 | 410.6 | |
8 | 13.7 | 1.3 | 2.3 | 7.3 | 619.7 | 1.4 | 52.4 | 21.2 | 0.6 | 509.5 | 598.9 | |
9 | 688.2 | 5.3 | 14.5 | 55.1 | 28.6 | 2.1 | 589.5 | 114.6 | 3.0 | 652.3 | 1131.9 | |
10 | 28.7 | 10.3 | 21.9 | 17.8 | 28.4 | 4.3 | 44.2 | 14.3 | 8.3 | 1392.9 | 2205.1 | |
11 | 21.0 | 6.2 | 10.5 | 35.1 | 55.0 | 1.8 | 765.7 | 340.9 | 4.2 | 630.0 | 1814.0 | |
12 | 2.8 | 2.3 | 2.3 | 37.1 | 82.6 | 1.2 | 126.5 | 29.9 | 8.1 | 467.4 | 1445.0 | |
S C R < 3 | 1 | 25.5 | 4.9 | 26.4 | 51.0 | 46.4 | 2.6 | 33.5 | 12.5 | 4.9 | 708.6 | 1163.0 |
2 | 22.5 | 8.0 | 9.3 | 42.2 | 20.5 | 1.4 | 147.9 | 29.2 | 5.2 | 1910.9 | 887.6 | |
3 | 22.2 | 4.0 | 5.6 | 17.5 | 7.2 | 1.6 | 56.6 | 31.0 | 1.4 | 501.0 | 1045.2 | |
4 | 15.1 | 2.7 | 6.9 | 24.5 | 6.4 | 1.5 | 13.0 | 16.1 | 1.8 | 965.8 | 1164.4 | |
5 | 137.5 | 2.1 | 3.3 | 39.0 | 8.7 | 1.4 | 96.7 | 11.1 | 1.6 | 352.3 | 545.8 | |
6 | 84.7 | 8.7 | 16.5 | 32.1 | 26.5 | 2.1 | 204.6 | 27.3 | 4.4 | 487.7 | 1768.7 | |
7 | 104.9 | 4.7 | 6.7 | 74.7 | 622.7 | 2.3 | 414.8 | 74.7 | 3.6 | 857.6 | 1687.2 | |
8 | 1054.4 | 6.0 | 12.7 | 85.8 | 38.9 | 2.2 | 624.2 | 502.9 | 4.1 | 1341.5 | 1629.2 |
Seq. | WSLCM | RLCM | TLLCM | NIPPS | RIPT | STLCF | ISTDUNet | RISTDNet | DNANet | DTUMNet | STIDNet | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
S C R > 3 | 1 | 1313.7 | 1.0 | 277.2 | 1518.3 | 614.1 | 3.4 | 578.4 | 24.9 | 1.1 | 1022.4 | 1241.5 |
2 | 2194.1 | 1.5 | 1492.1 | 1746.6 | 951.8 | 2.4 | 68.9 | 5.3 | 0.1 | 659.4 | 1127.2 | |
3 | 2806.6 | 1.6 | 1718.9 | 1239.0 | 1391 | 4.0 | 919.3 | 1053.9 | 1.8 | 1133.9 | 1817.0 | |
4 | 0.0 | 0.3 | 182.7 | 14.8 | 0.0 | 5.8 | 256.8 | 88.0 | 2.3 | 9490.6 | 6081.9 | |
5 | 45.9 | 1.4 | 3.5 | 16.4 | 0.0 | 1.2 | 33.9 | 8.6 | 0.3 | 1430.9 | 1620.5 | |
6 | 753.0 | 1.4 | 819.3 | 190.1 | 0.0 | 1.3 | 4.4 | 5.5 | 0.2 | 438.7 | 221.8 | |
7 | 535.6 | 1.2 | 882.4 | 61.6 | 26.1 | 1.0 | 8.3 | 4.5 | 0.1 | 422.4 | 203.1 | |
8 | 793.9 | 0.7 | 1352.1 | 918.6 | 421.3 | 1.6 | 27.9 | 14.1 | 0.2 | 1141.4 | 651.4 | |
9 | 1664.2 | 1.2 | 149.6 | 1107.8 | 1235 | 2.2 | 417.1 | 56.0 | 1.0 | 1004.7 | 1381.4 | |
10 | 0.4 | 0.1 | 7.1 | 5.2 | 0.0 | 2.0 | 6.0 | 28.2 | 0.7 | 5321.5 | 2736.5 | |
11 | 0.0 | 0.1 | 145.3 | 666.2 | 633.2 | 4.3 | 683.0 | 215.7 | 3.0 | 2859.4 | 2466.1 | |
12 | 0.0 | 0.0 | 232214 | 58.0 | 1571 | 1412.7 | 25336.6 | 18766.9 | 2130.7 | 7416719 | 398266 | |
S C R < 3 | 1 | 20.8 | 0.6 | 12.3 | 18.8 | 5.0 | 2.9 | 131.9 | 39.9 | 0.8 | 3263.6 | 3281.2 |
2 | 0.8 | 0.8 | 2.9 | 0.0 | 0.0 | 1.1 | 251.7 | 1.1 | 1.1 | 6947.3 | 4837.8 | |
3 | 6.6 | 0.6 | 18.1 | 231.9 | 141.4 | 3.1 | 360.2 | 73.3 | 1.6 | 4968.6 | 10788 | |
4 | 1.5 | 0.00 | 36.0 | 0.0 | 0.0 | 2.1 | 16.5 | 6.6 | 0.6 | 4408.6 | 4652.2 | |
5 | 1094.3 | 0.8 | 27.7 | 1085.5 | 549.2 | 1.4 | 20.6 | 15.3 | 0.7 | 593.1 | 847.8 | |
6 | 832.5 | 1.6 | 112.5 | 485.3 | 789.3 | 1.2 | 65.0 | 105.0 | 0.9 | 454.4 | 1340.9 | |
7 | 1275.7 | 1.1 | 23.8 | 890.2 | 961.4 | 1.3 | 64.8 | 89.0 | 0.6 | 715.6 | 566.5 | |
8 | 1440.6 | 0.9 | 68.4 | 1053.6 | 1117.3 | 2.1 | 72.4 | 487.7 | 1.0 | 1235.3 | 1101.9 |
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Zhang, L.; Zhou, Z.; Xi, Y.; Tan, F.; Hou, Q. STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets. Remote Sens. 2025, 17, 250. https://doi.org/10.3390/rs17020250
Zhang L, Zhou Z, Xi Y, Tan F, Hou Q. STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets. Remote Sensing. 2025; 17(2):250. https://doi.org/10.3390/rs17020250
Chicago/Turabian StyleZhang, Liuwei, Zhitao Zhou, Yuyang Xi, Fanjiao Tan, and Qingyu Hou. 2025. "STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets" Remote Sensing 17, no. 2: 250. https://doi.org/10.3390/rs17020250
APA StyleZhang, L., Zhou, Z., Xi, Y., Tan, F., & Hou, Q. (2025). STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets. Remote Sensing, 17(2), 250. https://doi.org/10.3390/rs17020250