Journal of Infrared and Millimeter Waves, Volume. 43, Issue 6, 859(2024)
Progressive spatio-temporal feature fusion network for infrared small-dim target detection
Fig. 1. Progressive spatio-temporal feature fusion network structure:(a)overall architecture of PSTFNet;(b)progressive temporal accumalation module;(c)multi-scale spatial feature fusion module
Fig. 2. Progressive temporal accumulation module:(a)architecture of the P2DConv module;(b)architecture of the M3DConv module
Fig. 3. SHU-MIRST dataset simulation flowchart:(a)background shooting;(b)target template production;(c)target 3D modeling;(d)image fusion algorithm for region resampling;(e)target template embedding
Fig. 4. SHU-MIRST dataset statistical information: (a) distribution of target sizes;(b) distribution of mean SCR
Fig. 5. Examples of target motion trajectory in the SHU-MIRST dataset
Fig. 6. ROC curves of PSTFNet under different mSCR: (a) mSCR≤3;(b) mSCR>3;(c) all sequences
Fig. 7. Qualitative comparison results of PSTFNet and 6 benchmark algorithms on the SHU-MIRST Dataset
Fig. 8. Visualization map of PSTFNet and the backbone network ResUNet at different stage of decoder
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Dan ZENG, Jian-Ming WEI, Jun-Jie ZHANG, Liang CHANG, Wei HUANG. Progressive spatio-temporal feature fusion network for infrared small-dim target detection[J]. Journal of Infrared and Millimeter Waves, 2024, 43(6): 859
Category: Interdisciplinary Research on Infrared Science
Received: Mar. 24, 2024
Accepted: --
Published Online: Dec. 13, 2024
The Author Email: HUANG Wei (lyxhw@shu.edu.cn)