- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LI Hengchao, LIU Yanqiong, YIN Jiajie, LEI Sen. Infrared Small Target Detection Based on Temporal Local Spatial Entropy and Spatial Multi-Scale Feature[J]. Journal of Southwest Jiaotong University, 2025, 60(6): 1527-1536. doi: 10.3969/j.issn.0258-2724.20240225
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Infrared imaging technology is widely used in military and civilian fields. As an indispensable part of the application, infrared small target detection has important practical significance. However, the existing methods hardly distinguish the real target from the target-like sparse structure. Therefore, an infrared small target detection algorithm was proposed by fusing the temporal local spatial entropy and the spatial multi-scale feature. In the temporal branch, a density peak clustering algorithm based on the similarity measurement of patches was designed to locate the candidate region of infrared small targets and reduce redundant calculation of backgrounds. Moreover, temporal local spatial entropy based on the local difference between frames was proposed to explore the variations in target and background entropy values in local regions and solve the false alarm caused by the target-like sparse structure. In addition, the spatial multi-scale feature branch was introduced to construct the spatio-temporal fusion feature, reducing the missed detection rate in the location of candidate regions and improving the detection ability of small targets at different scales. Compared with that of nine algorithms on five sets of sequences, the background suppression factor (BSF) of the proposed method is superior. The BSF is 2.02 times better than that of the suboptimal method on the best-performing sequence 5, and it is optimal on four sets of sequences in the receiver operating characteristic curve (ROC). In summary, compared with other methods, the proposed method can detect small targets accurately under the target-like sparse structure.
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