HGXES: Lightweight Network for Ship Detection in Specific Marine Environments.
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| Title: | HGXES: Lightweight Network for Ship Detection in Specific Marine Environments. |
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| Authors: | Tian, Yang1,2,3 (AUTHOR), Gao, Fei1,2,3 (AUTHOR) gaof0105@stdu.edu.cn, Huang, Rongfeng1,2,3 (AUTHOR), Wu, Yongliang1,2,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1276. 24p. |
| Subjects: | Synthetic aperture radar, Real-time computing, Image segmentation, Marine pollution monitoring, Feature extraction, Computing platforms |
| Abstract: | Highlights: What are the main findings? The HGXES model, a lightweight SAR ship detection network, achieves a 70% reduction in parameters compared to traditional models. This reduction comes from both the lightweight HGNetV2 backbone and the further lightweight incremental designs (Xfeat, LWDetect) proposed in this paper. The model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism. These are incremental innovations which significantly improve feature extraction and detection accuracy that of the baseline HGNetV2. What are the implications of the main findings? The HGXES model offers a promising solution for real-time SAR ship detection on resource-constrained platforms, enhancing marine monitoring capabilities with its lightweight and efficient design. The introduction of the ELA attention mechanism and ShapeIoU loss function into the HGXES model provides new insights into optimizing feature representation and boundary regression for improved detection in complex SAR image scenarios. Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The HGXES model, a lightweight SAR ship detection network, achieves a 70% reduction in parameters compared to traditional models. This reduction comes from both the lightweight HGNetV2 backbone and the further lightweight incremental designs (Xfeat, LWDetect) proposed in this paper. The model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism. These are incremental innovations which significantly improve feature extraction and detection accuracy that of the baseline HGNetV2. What are the implications of the main findings? The HGXES model offers a promising solution for real-time SAR ship detection on resource-constrained platforms, enhancing marine monitoring capabilities with its lightweight and efficient design. The introduction of the ELA attention mechanism and ShapeIoU loss function into the HGXES model provides new insights into optimizing feature representation and boundary regression for improved detection in complex SAR image scenarios. Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091276 |
