TPNet: A High-Performance and Lightweight Detector for Ship Detection in SAR Imagery
The advancement of SAR satellites enables continuous and real-time ship monitoring on water surfaces regardless of time and weather. Traditional ship detection algorithms in SAR imagery using manually designed operators lack accuracy, while many existing deep learning-based detection algorithms are...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/9/1487 |
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| Summary: | The advancement of SAR satellites enables continuous and real-time ship monitoring on water surfaces regardless of time and weather. Traditional ship detection algorithms in SAR imagery using manually designed operators lack accuracy, while many existing deep learning-based detection algorithms are computationally intensive and have room for accuracy improvement. Inspired by CenterNet, we propose the Three Points Network (TPNet). It locates the ship’s center point and estimates distances to the top-left and bottom-right corners for precise positioning. We introduce several innovative mechanisms to enhance TPNet’s performance, improving both accuracy and computational efficiency. Evaluated on the open-source SAR-Ship-Dataset, TPNet outperforms 14 other deep learning-based detection algorithms in accuracy and efficiency. Its strong generalization ability is further verified on SSDD and HRSID datasets. These results show TPNet’s potential in real-time maritime surveillance and monitoring systems. |
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| ISSN: | 2072-4292 |