SVDD: SAR Vehicle Dataset Construction and Detection
With the advent of high-quality SAR images and the rapid development of computing technology, the object detection algorithms based on convolution neural network have attracted a lot of attention in the field of SAR object detection. At present, the main dataset for SAR target detection in China foc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10848068/ |
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| Summary: | With the advent of high-quality SAR images and the rapid development of computing technology, the object detection algorithms based on convolution neural network have attracted a lot of attention in the field of SAR object detection. At present, the main dataset for SAR target detection in China focus on ships, there is a lack of SAR vehicle detect datasets, and complex ground scenes can affect vehicle detection performance. To solve these problems, we proposed a lightweight SAR vehicle detection algorithm, aiming to improve the vehicle detection accuracy and simplify the model complexity. First, we constructed a multi-band SAR vehicle detection dataset (SVDD) with annotations as the training dataset of the object detection model. Then, we introduced dual conv into the RT-DETR model. Dual conv uses group convolution technology to filter the convolutional network to reduce model parameters, so we can achieve a lightweight and real-time end-to-end detection. Finally, we used the mmdetection framework as a benchmark and test the robust performance under different conditions. Experimental results show that the AP50 of we proposed method reaches 98.5%, achieving excellent detection performance. |
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| ISSN: | 2169-3536 |