DSEE-YOLO: A Dynamic Edge-Enhanced Lightweight Model for Infrared Ship Detection in Complex Maritime Environments.
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| Title: | DSEE-YOLO: A Dynamic Edge-Enhanced Lightweight Model for Infrared Ship Detection in Complex Maritime Environments. |
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| Authors: | Wang, Siyu1,2,3 (AUTHOR), Feng, Yunsong1,2,3 (AUTHOR) fengyunsong17@nudt.edu.cn, Jin, Wei1,2,3 (AUTHOR), Liu, Liping1,2,3 (AUTHOR), Zhou, Changqi1,2,3 (AUTHOR), Tao, Huifeng1,2,3 (AUTHOR), Cai, Lei1,2,3 (AUTHOR) |
| Source: | Remote Sensing. Oct2025, Vol. 17 Issue 19, p3325. 26p. |
| Subjects: | Detection algorithms, Infrared technology, Object recognition (Computer vision), Maritime management, Real-time computing |
| Abstract: | Highlights: What are the main findings? We propose DSEE-YOLO, a lightweight infrared ship detection model integrating C3k2_MultiScaleEdgeFusion, DS_ADown, and DyTaskHead, plus pruning and BCKD self-distillation, and it outperforms YOLOv11n by 2.8% in mAP@0.50, reduces parameters by 42.3%, and shrinks the model size to 3.5 MB. On the self-constructed IRShip dataset, DSEE-YOLO achieves 92.2% precision, 85.9% recall, and 65.8% mAP@0.50:0.95, effectively addressing blurred features and background interference in complex maritime infrared scenes. What are the implications of the main findings? DSEE-YOLO's lightweight and highly efficient features demonstrate great potential for real-time deployment on resource-constrained devices, providing strong technical support for meeting the practical application needs of infrared ship detection in maritime monitoring scenarios. The model provides a novel technical paradigm that is both accurate and lightweight in infrared target detection, offering a reliable solution for all-weather maritime security, intelligent shipping, and coastal law enforcement. Complex marine infrared images, which suffer from background interference, blurred features, and indistinct contours, hamper detection accuracy. Meanwhile, the limited computing power, storage, and energy of maritime devices require target detection models suitable for real-time detection. To address these issues, we propose DSEE-YOLO (Dynamic Ship Edge-Enhanced YOLO), an efficient lightweight infrared ship detection algorithm. It integrates three innovative modules with pruning and self-distillation: the C3k2_MultiScaleEdgeFusion module replaces the original bottleneck with a MultiEdgeFusion structure to boost edge feature expression; the lightweight DS_ADown module uses DSConv (depthwise separable convolution) to reduce parameters while preserving feature capability; and the DyTaskHead dynamically aligns classification and localization features through task decomposition. Redundant structures are pruned via LAMP (Layer-Adaptive Sparsity for the Magnitude-Based Pruning), and performance is optimized via BCKD (Bridging Cross-Task Protocol Inconsistency for Knowledge Distillation) self-distillation, yielding a lightweight, efficient model. Experimental results show the DSEE-YOLO outperforms YOLOv11n when applied to our self-constructed IRShip dataset by reducing parameters by 42.3% and model size from 10.1 MB to 3.5 MB while increasing mAP@0.50 by 2.8%, mAP@0.50:0.95 by 3.8%, precision by 2.3%, and recall by 3.0%. These results validate its high-precision detection capability and lightweight advantages in complex infrared scenarios, offering an efficient solution for real-time maritime infrared ship monitoring. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose DSEE-YOLO, a lightweight infrared ship detection model integrating C3k2_MultiScaleEdgeFusion, DS_ADown, and DyTaskHead, plus pruning and BCKD self-distillation, and it outperforms YOLOv11n by 2.8% in mAP@0.50, reduces parameters by 42.3%, and shrinks the model size to 3.5 MB. On the self-constructed IRShip dataset, DSEE-YOLO achieves 92.2% precision, 85.9% recall, and 65.8% mAP@0.50:0.95, effectively addressing blurred features and background interference in complex maritime infrared scenes. What are the implications of the main findings? DSEE-YOLO's lightweight and highly efficient features demonstrate great potential for real-time deployment on resource-constrained devices, providing strong technical support for meeting the practical application needs of infrared ship detection in maritime monitoring scenarios. The model provides a novel technical paradigm that is both accurate and lightweight in infrared target detection, offering a reliable solution for all-weather maritime security, intelligent shipping, and coastal law enforcement. Complex marine infrared images, which suffer from background interference, blurred features, and indistinct contours, hamper detection accuracy. Meanwhile, the limited computing power, storage, and energy of maritime devices require target detection models suitable for real-time detection. To address these issues, we propose DSEE-YOLO (Dynamic Ship Edge-Enhanced YOLO), an efficient lightweight infrared ship detection algorithm. It integrates three innovative modules with pruning and self-distillation: the C3k2_MultiScaleEdgeFusion module replaces the original bottleneck with a MultiEdgeFusion structure to boost edge feature expression; the lightweight DS_ADown module uses DSConv (depthwise separable convolution) to reduce parameters while preserving feature capability; and the DyTaskHead dynamically aligns classification and localization features through task decomposition. Redundant structures are pruned via LAMP (Layer-Adaptive Sparsity for the Magnitude-Based Pruning), and performance is optimized via BCKD (Bridging Cross-Task Protocol Inconsistency for Knowledge Distillation) self-distillation, yielding a lightweight, efficient model. Experimental results show the DSEE-YOLO outperforms YOLOv11n when applied to our self-constructed IRShip dataset by reducing parameters by 42.3% and model size from 10.1 MB to 3.5 MB while increasing mAP@0.50 by 2.8%, mAP@0.50:0.95 by 3.8%, precision by 2.3%, and recall by 3.0%. These results validate its high-precision detection capability and lightweight advantages in complex infrared scenarios, offering an efficient solution for real-time maritime infrared ship monitoring. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17193325 |
