Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields
Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/307 |
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| author | Xiaozhen Wang Chengshan Han Jiaqi Li Ting Nie Mingxuan Li Xiaofeng Wang Liang Huang |
| author_facet | Xiaozhen Wang Chengshan Han Jiaqi Li Ting Nie Mingxuan Li Xiaofeng Wang Liang Huang |
| author_sort | Xiaozhen Wang |
| collection | DOAJ |
| description | Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics. |
| format | Article |
| id | doaj-art-d910274cd7e9431bb3f78f1062acec49 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d910274cd7e9431bb3f78f1062acec492025-01-24T13:48:04ZengMDPI AGRemote Sensing2072-42922025-01-0117230710.3390/rs17020307Infrared Dim Small Target Detection Algorithm with Large-Size Receptive FieldsXiaozhen Wang0Chengshan Han1Jiaqi Li2Ting Nie3Mingxuan Li4Xiaofeng Wang5Liang Huang6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaInfrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics.https://www.mdpi.com/2072-4292/17/2/307convolutional neural networklarge-size convolutional layersinfrared imagesmall-target detection |
| spellingShingle | Xiaozhen Wang Chengshan Han Jiaqi Li Ting Nie Mingxuan Li Xiaofeng Wang Liang Huang Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields Remote Sensing convolutional neural network large-size convolutional layers infrared image small-target detection |
| title | Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields |
| title_full | Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields |
| title_fullStr | Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields |
| title_full_unstemmed | Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields |
| title_short | Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields |
| title_sort | infrared dim small target detection algorithm with large size receptive fields |
| topic | convolutional neural network large-size convolutional layers infrared image small-target detection |
| url | https://www.mdpi.com/2072-4292/17/2/307 |
| work_keys_str_mv | AT xiaozhenwang infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT chengshanhan infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT jiaqili infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT tingnie infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT mingxuanli infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT xiaofengwang infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields AT lianghuang infrareddimsmalltargetdetectionalgorithmwithlargesizereceptivefields |