Hardware-Accelerated Infrared Small Target Recognition Based on Energy-Weighted Local Uncertainty Measure
Source abstract
Abstract
Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrared Target Detection Methodologies · Advanced Measurement and Detection Methods · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100090, China
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Funding
National Key R&D Program of China
References (25)
Show source-provided references
1. Li, C.X., Lin, S.J., and Chang, K.L. (2021, January 12–15). FPGA Implementation of Infrared Images Small Targets Track-Before-Detect System. Proceedings of the 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), Kyoto, Japan.10.1109/GCCE53005.2021.9621850 ↗
2. Wu, Y., Yan, H., and Wang, M. (2020, January 7–8). Detection algorithm of infrared dim small target based on FPGA. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.10.1109/CAC51589.2020.9327527 ↗
3. The method of infrared point target detection and tracking based on DSP + FPGA10.4028/www.scientific.net/AMM.599-601.1272 ↗
4. Bo, M., Hui, Z., Zheng, M., Ang, L., Wenyang, J., and Weijun, M. (2015, January 16–18). FPGA implementation of local contrast method for infrared small target detection. Proceedings of the 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Qingdao, China.
5. Zhang, Y., Gao, J., Yang, X., and Yang, C. (2022, January 16–19). Hardware acceleration of infrared small target detection based on FPGA. Proceedings of the 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), Chengdu, China.10.1109/ICIEA54703.2022.10005901 ↗
6. Roy, P., Das, D., Dash, P., and Chauhan, M. (2019, January 15–17). Hardware Software Co-Design for Real Time Detection of Small Target in IR Video. Proceedings of the 2019 International Conference on Range Technology (ICORT), Balasore, India.10.1109/ICORT46471.2019.9069610 ↗
7. Real-time infrared small target detection network and accelerator design10.1016/j.vlsi.2022.07.008 ↗
8. Infrared small target segmentation networks: A survey10.1016/j.patcog.2023.109788 ↗
9. Zhang, Z., Ding, C., Gao, Z., and Xie, C. (2023). Anlpt: Self-adaptive and non-local patch-tensor model for infrared small target detection. Remote Sens., 15.10.3390/rs15041021 ↗
10. Review of infrared image complexity evaluation method
11. Point target detection based on multiscale morphological filtering and an energy concentration criterion10.1364/AO.56.006796 ↗
12. Point target detection and subpixel position estimation in optical imagery10.1364/AO.43.000257 ↗
13. Infrared small target energy distribution modeling for 2D subpixel motion and target energy compensation detection10.1117/1.OE.61.1.013104 ↗
14. Object detection in 20 years: A survey10.1109/JPROC.2023.3238524 ↗
15. Methods for small, weak object detection in optical high-resolution remote sensing images: A survey of advances and challenges10.1109/MGRS.2020.3041450 ↗
16. A review of visual moving target tracking10.1007/s11042-016-3647-0 ↗
17. Single-frame infrared small-target detection: A survey10.1109/MGRS.2022.3145502 ↗
18. Moradi, S., Memarmoghadam, A., Moallem, P., and Sabahi, M.F. (2023). Assessing the applicability of common performance metrics for real-world infrared small-target detection. arXiv.
19. Metrics for performance evaluation of preprocessing algorithms in infrared small target images10.2528/PIER11012412 ↗
20. Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data10.1109/83.236534 ↗
21. Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles10.1038/s41598-022-14355-y ↗
22. Singh, N., and Singh, P. (2021, January 24–26). Exploring the effect of normalization on medical data classification. Proceedings of the 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV), Gandhinagar, India.10.1109/AIMV53313.2021.9670938 ↗
23. Novel detection method for infrared small targets using weighted information entropy10.1109/JSEE.2012.00102 ↗
24. Hui, B., Song, Z., Fan, H., Zhong, P., Hu, W., Zhang, X., Lin, J., Su, H., Jin, W., and Zhang, Y. (2019). A Dataset for Infrared Image Dim-Small Aircraft Target Detection and Tracking under Ground/Air Background, Science Data Bank.
25. A real-time tracking system of infrared dim and small target based on FPGA and DSP