Enhancing concealed object detection in active THz security images with adaptation-YOLO
Abstract The terahertz (THz) security scanner offers advantages such as non-contact inspection and the ability to detect various types of dangerous goods, playing an important role in preventing terrorist attacks. We aim to accurately and quickly detect concealed objects in THz security images. Howe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81054-1 |
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| _version_ | 1832585893748146176 |
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| author | Aiguo Cheng Shiyou Wu Xiaodong Liu Hangyu Lu |
| author_facet | Aiguo Cheng Shiyou Wu Xiaodong Liu Hangyu Lu |
| author_sort | Aiguo Cheng |
| collection | DOAJ |
| description | Abstract The terahertz (THz) security scanner offers advantages such as non-contact inspection and the ability to detect various types of dangerous goods, playing an important role in preventing terrorist attacks. We aim to accurately and quickly detect concealed objects in THz security images. However, current object detection algorithms face many challenges when applied to THz images. The main reasons for the detection difficulty are that the concealed objects are small, the image resolution is low, and there is back-ground noise. Many methods often ignore the contextual dependency of the objects, hindering the effective capture of the object’s features. To address this task, this paper first proposes an adaptive context-aware attention network (ACAN), which models global contextual association features in both spatial and channel dimensions. By dynamically combining local features and their global relationships, contextual association information can be obtained from the input features, and enhanced attention features can be achieved through feature fusion to enable precise detection of concealed objects. Secondly, we improved the adaptive convolution and developed the dynamic adaptive convolution block (DACB). DACB can adaptively adjust convolution filter parameters and allocate the filters to the corresponding spatial regions, then filter the feature maps to suppress interference information. Finally, we integrated these two components to YOLOv8, resulting in Adaptation-YOLO. Through wide-ranging experiments on the active THz image dataset, the results demonstrate that the suggested method effectively improves the accuracy and efficiency of object detectors. |
| format | Article |
| id | doaj-art-0f841cc323fd4ecf90c3a8d4226c3ed3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0f841cc323fd4ecf90c3a8d4226c3ed32025-01-26T12:23:46ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-81054-1Enhancing concealed object detection in active THz security images with adaptation-YOLOAiguo Cheng0Shiyou Wu1Xiaodong Liu2Hangyu Lu3Aerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAbstract The terahertz (THz) security scanner offers advantages such as non-contact inspection and the ability to detect various types of dangerous goods, playing an important role in preventing terrorist attacks. We aim to accurately and quickly detect concealed objects in THz security images. However, current object detection algorithms face many challenges when applied to THz images. The main reasons for the detection difficulty are that the concealed objects are small, the image resolution is low, and there is back-ground noise. Many methods often ignore the contextual dependency of the objects, hindering the effective capture of the object’s features. To address this task, this paper first proposes an adaptive context-aware attention network (ACAN), which models global contextual association features in both spatial and channel dimensions. By dynamically combining local features and their global relationships, contextual association information can be obtained from the input features, and enhanced attention features can be achieved through feature fusion to enable precise detection of concealed objects. Secondly, we improved the adaptive convolution and developed the dynamic adaptive convolution block (DACB). DACB can adaptively adjust convolution filter parameters and allocate the filters to the corresponding spatial regions, then filter the feature maps to suppress interference information. Finally, we integrated these two components to YOLOv8, resulting in Adaptation-YOLO. Through wide-ranging experiments on the active THz image dataset, the results demonstrate that the suggested method effectively improves the accuracy and efficiency of object detectors.https://doi.org/10.1038/s41598-024-81054-1Concealed object detectionAttention mechanismAdaptive convolutionActive terahertz (THz) security image |
| spellingShingle | Aiguo Cheng Shiyou Wu Xiaodong Liu Hangyu Lu Enhancing concealed object detection in active THz security images with adaptation-YOLO Scientific Reports Concealed object detection Attention mechanism Adaptive convolution Active terahertz (THz) security image |
| title | Enhancing concealed object detection in active THz security images with adaptation-YOLO |
| title_full | Enhancing concealed object detection in active THz security images with adaptation-YOLO |
| title_fullStr | Enhancing concealed object detection in active THz security images with adaptation-YOLO |
| title_full_unstemmed | Enhancing concealed object detection in active THz security images with adaptation-YOLO |
| title_short | Enhancing concealed object detection in active THz security images with adaptation-YOLO |
| title_sort | enhancing concealed object detection in active thz security images with adaptation yolo |
| topic | Concealed object detection Attention mechanism Adaptive convolution Active terahertz (THz) security image |
| url | https://doi.org/10.1038/s41598-024-81054-1 |
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