Zhao-Xu LI, Qing-Xu XU, Wei AN, Xu HE, Gao-Wei GUO, Miao LI, Qiang LING, Long-Guang WANG, Chao XIAO, Zai-Ping LIN. A lightweight dark object detection network for infrared images[J]. Journal of Infrared and Millimeter Waves, 2025, 44(2): 285
- Journal of Infrared and Millimeter Waves
- Vol. 44, Issue 2, 285 (2025)
Fig. 1. The thermal infrared images of real civial airplanes capured by SDGSAT-1：（a）8~10.5 μm；（b）10.3~11.3 μm；（c）11.5~12.5 μm
Fig. 2. Schematic diagram of AirFormer network structure
Fig. 3. Schematic diagram of the calculation for object abundance matrix: (a) object shape modeling; (b) shape model embedding in image; (c) object abundance matrix calculation; (d) Gaussian blurring of the abundance matrix
Fig. 4. The 1st real civial aircraft and its simulation: (a) simulated image; (b) real object; (c) simulated object
Fig. 5. The 5th real civial aircraft and its simulation: (a) simulated image; (b) real object; (c) simulated object
Fig. 6. Simulated sequence examples: (a) sequence 0041; (b) sequence 0077; (c) sequence 0266; (d) sequence 0393
Fig. 7. The detection results of AirFormer for real civil airports: (a) the 1st real airport; (b) the 2nd real airport; (c) the 3rd real airport; (d) the 4th real airport; (e) the 5th real airport
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Table 1. The imaging information of real civial airplanes
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Table 2. Grayscale value information of real civial airplanes and background
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Table 3. Simulation parameter settings for real objects
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Table 4. Sample numbers under different load parameters on the IRAir dataset
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Table 5. The parameter settings of example simulated sequences
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Table 6. Performance comparison of detection methods
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Table 7. Detection performance comparison of AirFormer for objects with different sizes
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