Design and Optimization of an FPGA-Based Infrared Dim Small Target Detection Network Under a Sky Cloud Background
Source abstract
Abstract
To address the challenges of infrared dim small target detection under sky cloud backgrounds on edge devices, this study proposes a lightweight sequential-differential-frame-based network (LSDF-Net) with optimization and deployment on the heterogeneous FPGA JFMQL100TAI. The network enhances detection performance through sequential-differential inputs, false-alarm-object learning, and multi-anchor assignment while reducing computational overhead through sequential-differential acceleration and convolutional pooling. Deployment efficiency is improved via image channel optimization, mixed quantization, and refining the infrared image calibration set. Experimental results indicate that the proposed network structure optimization methods reduce the hardware inference time by 15.78%. Overall, the optimized LSDF-Net achieves a recall rate of no less than 85.71% on the validation datasets and an FPS of 54.10 on the JFMQL100TAI. The proposed methods provide a reference solution for related application fields.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrared Target Detection Methodologies · Advanced Measurement and Detection Methods · Optical Systems and Laser Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chinese Academy of Sciences, Chengdu 610209, China
National Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
University of Chinese Academy of Sciences, Beijing 100049, China
References (41)
Show source-provided references
1. Wu, Z., Fuller, N., Theriault, D., and Betke, M. (2014, January 23–28). A Thermal Infrared Video Benchmark for Visual Analysis. Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.10.1109/CVPRW.2014.39 ↗
2. Anti-interference small target tracking from infrared dual waveband imagery10.1016/j.infrared.2021.103882 ↗
3. Infrared ship target segmentation based on spatial information improved FCM10.1109/TCYB.2015.2501848 ↗
4. Barnett, J. (1989, January 16–17). Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds. Proceedings of the Infrared Systems and Components III, Los Angeles, CA, USA.10.1117/12.951425 ↗
5. Tom, V.T., Peli, T., Leung, M., and Bondaryk, J.E. (1993, January 12–14). Morphology-based algorithm for point target detection in infrared backgrounds. Proceedings of the 5th Conf on Signal and Data Processing of Small TargetsSignal & Data Processing of Small Targets, Orlando, FL, USA.10.1117/12.157758 ↗
6. Deshpande, S.D., Er, M.H., Ronda, V., and Chan, P. (1999, January 19–23). Max-Mean and Max-Median Filters for Detection of Small-Targets. Proceedings of the Conference on Signal and Data Processing of Small Targets 1999, Denver, CO, USA.10.1117/12.364049 ↗
7. Infrared Small Target Detection via Nonnegativity-Constrained Variational Mode Decomposition10.1109/LGRS.2017.2729512 ↗
8. Infrared Small Target Detection via Nonconvex Tensor Fibered Rank Approximation10.1109/TGRS.2021.3068465 ↗
9. High-Boost-Based Multiscale Local Contrast Measure for Infrared Small Target Detection10.1109/LGRS.2017.2772030 ↗
10. A Local Contrast Method Combined with Adaptive Background Estimation for Infrared Small Target Detection10.1109/LGRS.2019.2898893 ↗
11. A Local Contrast Method for Small Infrared Target Detection10.1109/TGRS.2013.2242477 ↗
12. A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window10.1109/LGRS.2019.2954578 ↗
13. Wang, X.T., Lu, R.T., Bi, H.X., and Li, Y.H. (2023). An Infrared Small Target Detection Method Based on Attention Mechanism. Sensors, 23.10.3390/s23208608 ↗
14. Infrared Patch-Image Model for Small Target Detection in a Single Image10.1109/TIP.2013.2281420 ↗
15. Infrared Target Tracking Based on Robust Low-Rank Sparse Learning10.1109/LGRS.2015.2506758 ↗
16. Reweighted Infrared Patch-Tensor Model with Both Nonlocal and Local Priors for Single-Frame Small Target Detection10.1109/JSTARS.2017.2700023 ↗
17. Zhang, L.D., Peng, L.B., Zhang, T.F., Cao, S.Y., and Peng, Z.M. (2018). Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm. Remote Sens., 10.10.3390/rs10111821 ↗
18. Attentional Local Contrast Networks for Infrared Small Target Detection10.1109/TGRS.2020.3044958 ↗
19. Pay Attention to Local Contrast Learning Networks for Infrared Small Target Detection10.1109/LGRS.2022.3178984 ↗
20. Gradient-Guided Learning Network for Infrared Small Target Detection10.1109/LGRS.2023.3308783 ↗
21. Infrared Small Object Detection Using Deep Interactive U-Net
22. RISTDnet: Robust Infrared Small Target Detection Network
23. Tong, X.Z., Sun, B., Wei, J.Y., Zuo, Z., and Su, S.J. (2021). EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection. Remote Sens., 13.10.3390/rs13163200 ↗
24. Dense Nested Attention Network for Infrared Small Target Detection10.1109/TIP.2022.3199107 ↗
25. CA-U2-Net: Contour Detection and Attention in U2-Net for Infrared Dim and Small Target Detection10.1109/ACCESS.2023.3305942 ↗
26. Wang, H., Zhou, L.P., and Wang, L. (November, January 27). Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.
27. BPR-Net: Balancing Precision and Recall for Infrared Small Target Detection10.1109/TGRS.2023.3296102 ↗
28. DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model10.1109/TIM.2022.3220285 ↗
29. Hu, Y., Liu, Y., and Liu, Z. (2022, January 7–9). A survey on convolutional neural network accelerators: GPU, FPGA and ASIC. Proceedings of the 2022 14th International Conference on Computer Research and Development (ICCRD), Shenzhen, China.10.1109/ICCRD54409.2022.9730377 ↗
30. Nurvitadhi, E., Sim, J., Sheffield, D., Mishra, A., Krishnan, S., and Marr, D. (September, January 29). Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC. Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland.10.1109/FPL.2016.7577314 ↗
31. Ranzato, M.A., Boureau, Y.-L., and Cun, Y. (2007, January 3–6). Sparse feature learning for deep belief networks. Proceedings of the Advances in Neural Information Processing Systems 20 (NIPS 2007), Vancouver, BC, Canada.
32. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989, January 27–30). Handwritten digit recognition with a back-propagation network. Proceedings of the Advances in Neural Information Processing Systems 2 (NIPS 1989), Denver, CO, USA.
33. He, L., Xie, L., Xie, T., Pan, H., and Zheng, Y. (2012, January 7–9). An effective TBD algorithm for the detection of infrared dim-small moving target in the sky scene. Proceedings of the Multimedia and Signal Processing: Second International Conference, CMSP 2012, Shanghai, China.10.1007/978-3-642-35286-7_32 ↗
34. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR.2016.91 ↗
35. A Strategy for Training Dim and Small Infrared Targets Detection Networks Under Sequential Cloud Background Images10.1109/ACCESS.2024.3435872 ↗
36. Tan, M., and Le, Q. (2019, January 9–15). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
37. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
38. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
39. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
40. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
41. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5 MB model size. arXiv.