SVDDD: SAR Vehicle Target Detection Dataset Augmentation Based on Diffusion Model
In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/286 |
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| Summary: | In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset. Unfortunately, due to various constraints, the availability of labeled image data for training SAR vehicle detection networks is quite limited. This scarcity of data has become one of the main obstacles hindering the further development of SAR vehicle detection. In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. In the experiment, the samples we generated and screened achieved an average improvement of 2.32%, with a maximum of 6.6% in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>75</mn></msub></mrow></semantics></math></inline-formula> on five different strong baseline detectors. |
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| ISSN: | 2072-4292 |