Ship Plate Detection Algorithm Based on Improved RT-DETR
To address the challenges in ship plate detection under complex maritime scenarios—such as small target size, extreme aspect ratios, dense arrangements, and multi-angle rotations—this paper proposes a multi-module collaborative detection algorithm, RT-DETR-HPA, based on an enhanced RT-DETR framework...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| author | Lei Zhang Liuyi Huang |
| author_facet | Lei Zhang Liuyi Huang |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | To address the challenges in ship plate detection under complex maritime scenarios—such as small target size, extreme aspect ratios, dense arrangements, and multi-angle rotations—this paper proposes a multi-module collaborative detection algorithm, RT-DETR-HPA, based on an enhanced RT-DETR framework. The proposed model integrates three core components: an improved High-Frequency Enhanced Residual Block (HFERB) embedded in the backbone to strengthen multi-scale high-frequency feature fusion, with deformable convolution added to handle occlusion and deformation; a Pinwheel-shaped Convolution (PConv) module employing multi-directional convolution kernels to achieve rotation-adaptive local detail extraction and accurately capture plate edges and character features; and an Adaptive Sparse Self-Attention (ASSA) mechanism incorporated into the encoder to automatically focus on key regions while suppressing complex background interference, thereby enhancing feature discriminability. Comparative experiments conducted on a self-constructed dataset of 20,000 ship plate images show that, compared to the original RT-DETR, RT-DETR-HPA achieves a 3.36% improvement in mAP@50 (up to 97.12%), a 3.23% increase in recall (reaching 94.88%), and maintains real-time detection speed at 40.1 FPS. Compared with mainstream object detection models such as the YOLO series and Faster R-CNN, RT-DETR-HPA demonstrates significant advantages in high-precision localization, adaptability to complex scenarios, and real-time performance. It effectively reduces missed and false detections caused by low resolution, poor lighting, and dense occlusion, providing a robust and high-accuracy solution for intelligent ship supervision. Future work will focus on lightweight model design and dynamic resolution adaptation to enhance its applicability on mobile maritime surveillance platforms. |
| format | Article |
| id | doaj-art-c9b01d52f1ba46448fbe3559f60fb14a |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c9b01d52f1ba46448fbe3559f60fb14a2025-08-20T03:58:26ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137127710.3390/jmse13071277Ship Plate Detection Algorithm Based on Improved RT-DETRLei Zhang0Liuyi Huang1College of Fishery, Ocean University of China, Qingdao 266003, ChinaCollege of Fishery, Ocean University of China, Qingdao 266003, ChinaTo address the challenges in ship plate detection under complex maritime scenarios—such as small target size, extreme aspect ratios, dense arrangements, and multi-angle rotations—this paper proposes a multi-module collaborative detection algorithm, RT-DETR-HPA, based on an enhanced RT-DETR framework. The proposed model integrates three core components: an improved High-Frequency Enhanced Residual Block (HFERB) embedded in the backbone to strengthen multi-scale high-frequency feature fusion, with deformable convolution added to handle occlusion and deformation; a Pinwheel-shaped Convolution (PConv) module employing multi-directional convolution kernels to achieve rotation-adaptive local detail extraction and accurately capture plate edges and character features; and an Adaptive Sparse Self-Attention (ASSA) mechanism incorporated into the encoder to automatically focus on key regions while suppressing complex background interference, thereby enhancing feature discriminability. Comparative experiments conducted on a self-constructed dataset of 20,000 ship plate images show that, compared to the original RT-DETR, RT-DETR-HPA achieves a 3.36% improvement in mAP@50 (up to 97.12%), a 3.23% increase in recall (reaching 94.88%), and maintains real-time detection speed at 40.1 FPS. Compared with mainstream object detection models such as the YOLO series and Faster R-CNN, RT-DETR-HPA demonstrates significant advantages in high-precision localization, adaptability to complex scenarios, and real-time performance. It effectively reduces missed and false detections caused by low resolution, poor lighting, and dense occlusion, providing a robust and high-accuracy solution for intelligent ship supervision. Future work will focus on lightweight model design and dynamic resolution adaptation to enhance its applicability on mobile maritime surveillance platforms.https://www.mdpi.com/2077-1312/13/7/1277ship plate detectionRT-DETRHigh-Frequency Enhanced Residual BlockPinwheel-shaped ConvolutionAdaptive Sparse Self-Attention |
| spellingShingle | Lei Zhang Liuyi Huang Ship Plate Detection Algorithm Based on Improved RT-DETR Journal of Marine Science and Engineering ship plate detection RT-DETR High-Frequency Enhanced Residual Block Pinwheel-shaped Convolution Adaptive Sparse Self-Attention |
| title | Ship Plate Detection Algorithm Based on Improved RT-DETR |
| title_full | Ship Plate Detection Algorithm Based on Improved RT-DETR |
| title_fullStr | Ship Plate Detection Algorithm Based on Improved RT-DETR |
| title_full_unstemmed | Ship Plate Detection Algorithm Based on Improved RT-DETR |
| title_short | Ship Plate Detection Algorithm Based on Improved RT-DETR |
| title_sort | ship plate detection algorithm based on improved rt detr |
| topic | ship plate detection RT-DETR High-Frequency Enhanced Residual Block Pinwheel-shaped Convolution Adaptive Sparse Self-Attention |
| url | https://www.mdpi.com/2077-1312/13/7/1277 |
| work_keys_str_mv | AT leizhang shipplatedetectionalgorithmbasedonimprovedrtdetr AT liuyihuang shipplatedetectionalgorithmbasedonimprovedrtdetr |