Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation
Dim and small target detection plays an important role in infrared target recognition systems. In this paper, we present a dim and small target detection algorithm based on improved bilateral filtering and Gaussian motion probability estimation, aiming to improve the detection efficiency of the dete...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10636302/ |
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| author | Fan Xiangsuo Qin Wenlin Feng Gaoshan Huang Qingnan Min Lei |
| author_facet | Fan Xiangsuo Qin Wenlin Feng Gaoshan Huang Qingnan Min Lei |
| author_sort | Fan Xiangsuo |
| collection | DOAJ |
| description | Dim and small target detection plays an important role in infrared target recognition systems. In this paper, we present a dim and small target detection algorithm based on improved bilateral filtering and Gaussian motion probability estimation, aiming to improve the detection efficiency of the detection system. First, a bilateral filtering algorithm based on image patch analysis is proposed to complete the background modeling, compare with single pixel, image patch contains more neighborhood information. Then, we use the Gaussian process combining the target position of consecutive <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> frames to predict the target position of the <inline-formula><tex-math notation="LaTeX">$(n+1)\text{th}$</tex-math></inline-formula> frame, and the target energy is accumulated along the trajectory direction at the same time. Finally, we construct the grayscale probability model to realize the multi-frame correlation detection, which combining the grayscale features and the motion characteristics of the target. Six scenes and eleven comparison algorithms are selected for experiments, experimental results show the effectiveness and robustness of the proposed algorithm. |
| format | Article |
| id | doaj-art-484e2cac03cd4abf9384cd296a15ed0e |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-484e2cac03cd4abf9384cd296a15ed0e2025-08-20T03:33:21ZengIEEEIEEE Photonics Journal1943-06552024-01-0116512010.1109/JPHOT.2024.344323910636302Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability EstimationFan Xiangsuo0https://orcid.org/0000-0002-1685-4989Qin Wenlin1https://orcid.org/0000-0003-0410-4184Feng Gaoshan2Huang Qingnan3https://orcid.org/0000-0002-0458-6630Min Lei4https://orcid.org/0000-0002-3459-6063School of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaDongfeng Liuzhou Motor Company, Ltd., Liuzhou, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaInstitute of Optics and Electronics Chinese Academy of Sciences, Chengdu, ChinaDim and small target detection plays an important role in infrared target recognition systems. In this paper, we present a dim and small target detection algorithm based on improved bilateral filtering and Gaussian motion probability estimation, aiming to improve the detection efficiency of the detection system. First, a bilateral filtering algorithm based on image patch analysis is proposed to complete the background modeling, compare with single pixel, image patch contains more neighborhood information. Then, we use the Gaussian process combining the target position of consecutive <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> frames to predict the target position of the <inline-formula><tex-math notation="LaTeX">$(n+1)\text{th}$</tex-math></inline-formula> frame, and the target energy is accumulated along the trajectory direction at the same time. Finally, we construct the grayscale probability model to realize the multi-frame correlation detection, which combining the grayscale features and the motion characteristics of the target. Six scenes and eleven comparison algorithms are selected for experiments, experimental results show the effectiveness and robustness of the proposed algorithm.https://ieeexplore.ieee.org/document/10636302/Bilateral filteringdim and small targetgaussian processmotion estimation |
| spellingShingle | Fan Xiangsuo Qin Wenlin Feng Gaoshan Huang Qingnan Min Lei Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation IEEE Photonics Journal Bilateral filtering dim and small target gaussian process motion estimation |
| title | Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation |
| title_full | Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation |
| title_fullStr | Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation |
| title_full_unstemmed | Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation |
| title_short | Dim and Small Target Detection Based on Improved Bilateral Filtering and Gaussian Motion Probability Estimation |
| title_sort | dim and small target detection based on improved bilateral filtering and gaussian motion probability estimation |
| topic | Bilateral filtering dim and small target gaussian process motion estimation |
| url | https://ieeexplore.ieee.org/document/10636302/ |
| work_keys_str_mv | AT fanxiangsuo dimandsmalltargetdetectionbasedonimprovedbilateralfilteringandgaussianmotionprobabilityestimation AT qinwenlin dimandsmalltargetdetectionbasedonimprovedbilateralfilteringandgaussianmotionprobabilityestimation AT fenggaoshan dimandsmalltargetdetectionbasedonimprovedbilateralfilteringandgaussianmotionprobabilityestimation AT huangqingnan dimandsmalltargetdetectionbasedonimprovedbilateralfilteringandgaussianmotionprobabilityestimation AT minlei dimandsmalltargetdetectionbasedonimprovedbilateralfilteringandgaussianmotionprobabilityestimation |