Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion
Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2130 |
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| _version_ | 1846104140664537088 |
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| author | Chun Liu Yuanliang Zhang Jingfu Shen Feiyue Liu |
| author_facet | Chun Liu Yuanliang Zhang Jingfu Shen Feiyue Liu |
| author_sort | Chun Liu |
| collection | DOAJ |
| description | Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue. |
| format | Article |
| id | doaj-art-58df9e53bc0140bbab36feb693e4523a |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-58df9e53bc0140bbab36feb693e4523a2024-12-27T14:33:02ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011212213010.3390/jmse12122130Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature FusionChun Liu0Yuanliang Zhang1Jingfu Shen2Feiyue Liu3College of Marine Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaCollege of Mechanical Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaCollege of Marine Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaCollege of Marine Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaInfrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue.https://www.mdpi.com/2077-1312/12/12/2130object detectioninfrared shipRT-DETR |
| spellingShingle | Chun Liu Yuanliang Zhang Jingfu Shen Feiyue Liu Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion Journal of Marine Science and Engineering object detection infrared ship RT-DETR |
| title | Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion |
| title_full | Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion |
| title_fullStr | Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion |
| title_full_unstemmed | Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion |
| title_short | Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion |
| title_sort | improved rt detr for infrared ship detection based on multi attention and feature fusion |
| topic | object detection infrared ship RT-DETR |
| url | https://www.mdpi.com/2077-1312/12/12/2130 |
| work_keys_str_mv | AT chunliu improvedrtdetrforinfraredshipdetectionbasedonmultiattentionandfeaturefusion AT yuanliangzhang improvedrtdetrforinfraredshipdetectionbasedonmultiattentionandfeaturefusion AT jingfushen improvedrtdetrforinfraredshipdetectionbasedonmultiattentionandfeaturefusion AT feiyueliu improvedrtdetrforinfraredshipdetectionbasedonmultiattentionandfeaturefusion |