Image Enhancement-Based Detection with Small Infrared Targets
Today, target detection has an indispensable application in various fields. Infrared small-target detection, as a branch of target detection, can improve the perception capability of autonomous systems, and it has good application prospects in infrared alarm, automatic driving and other fields. Ther...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/13/3232 |
| _version_ | 1827598619258126336 |
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| author | Shuai Liu Pengfei Chen Marcin Woźniak |
| author_facet | Shuai Liu Pengfei Chen Marcin Woźniak |
| author_sort | Shuai Liu |
| collection | DOAJ |
| description | Today, target detection has an indispensable application in various fields. Infrared small-target detection, as a branch of target detection, can improve the perception capability of autonomous systems, and it has good application prospects in infrared alarm, automatic driving and other fields. There are many well-established algorithms that perform well in infrared small-target detection. Nevertheless, the current algorithms cannot achieve the expected detection effect in complex environments, such as background clutter, noise inundation or very small targets. We have designed an image enhancement-based detection algorithm to solve both problems through detail enhancement and target expansion. This method first enhances the mutation information, detail and edge information of the image and then improves the contrast between the target edge and the adjacent pixels to make the target more prominent. The enhancement improves the robustness of detection with background clutter or noise-flooded scenes. Moreover, bicubic interpolation is used on the input image, and the target pixels are expanded with upsampling, which enhances the detection effectiveness for tiny targets. From the results of qualitative and quantitative experiments, the algorithm proposed in this paper outperforms the existing work on various evaluation indicators. |
| first_indexed | 2024-03-09T03:55:37Z |
| format | Article |
| id | doaj.art-70903a8e0b0d4e15aa811d9285dd985b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:55:37Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-70903a8e0b0d4e15aa811d9285dd985b2023-12-03T14:21:26ZengMDPI AGRemote Sensing2072-42922022-07-011413323210.3390/rs14133232Image Enhancement-Based Detection with Small Infrared TargetsShuai Liu0Pengfei Chen1Marcin Woźniak2Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha 410081, ChinaKey Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha 410081, ChinaFaculty of Applied Mathematics, Silesian University of Technology, 44100 Gliwice, PolandToday, target detection has an indispensable application in various fields. Infrared small-target detection, as a branch of target detection, can improve the perception capability of autonomous systems, and it has good application prospects in infrared alarm, automatic driving and other fields. There are many well-established algorithms that perform well in infrared small-target detection. Nevertheless, the current algorithms cannot achieve the expected detection effect in complex environments, such as background clutter, noise inundation or very small targets. We have designed an image enhancement-based detection algorithm to solve both problems through detail enhancement and target expansion. This method first enhances the mutation information, detail and edge information of the image and then improves the contrast between the target edge and the adjacent pixels to make the target more prominent. The enhancement improves the robustness of detection with background clutter or noise-flooded scenes. Moreover, bicubic interpolation is used on the input image, and the target pixels are expanded with upsampling, which enhances the detection effectiveness for tiny targets. From the results of qualitative and quantitative experiments, the algorithm proposed in this paper outperforms the existing work on various evaluation indicators.https://www.mdpi.com/2072-4292/14/13/3232small target detectioninfrared small targetautonomous systemsperception capabilitiesimage enhancementupsampling |
| spellingShingle | Shuai Liu Pengfei Chen Marcin Woźniak Image Enhancement-Based Detection with Small Infrared Targets Remote Sensing small target detection infrared small target autonomous systems perception capabilities image enhancement upsampling |
| title | Image Enhancement-Based Detection with Small Infrared Targets |
| title_full | Image Enhancement-Based Detection with Small Infrared Targets |
| title_fullStr | Image Enhancement-Based Detection with Small Infrared Targets |
| title_full_unstemmed | Image Enhancement-Based Detection with Small Infrared Targets |
| title_short | Image Enhancement-Based Detection with Small Infrared Targets |
| title_sort | image enhancement based detection with small infrared targets |
| topic | small target detection infrared small target autonomous systems perception capabilities image enhancement upsampling |
| url | https://www.mdpi.com/2072-4292/14/13/3232 |
| work_keys_str_mv | AT shuailiu imageenhancementbaseddetectionwithsmallinfraredtargets AT pengfeichen imageenhancementbaseddetectionwithsmallinfraredtargets AT marcinwozniak imageenhancementbaseddetectionwithsmallinfraredtargets |