Object Detection in Wideband SAR Images Using DiffusionDet

Target detection in wide-band SAR image faces problems with speckle noise and small target detection. This paper uses the DiffusionDet model to solve the problem. DiffusionDet transforms target detection into a denoising diffusion process from noise box to target box, which consists of an image encoder and a detection decoder. When implemented, the pre-trained image encoder is first used to extract SAR image features. Then, the diffusion model is applied to generate candidate positions for target detection boxes on the feature map. By adding noise to the true value box and learning the inversion process, the target box is gradually refined. In the training phase, a SAR image dataset with true value annotations is used for training. Finally, the optimized DiffusionDet model is applied to the wide-band SAR image target detection task, and the detection performance is improved by adjusting parameters. The maximum IoU value of the DiffusionDet model reaches 1, and the average detection time is only 289.325 milliseconds, which is far lower than Faster R-CNN's <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 3 4. 1 2 5}$</tex> milliseconds. In addition, the ablation experiment also reveals the trade-off between the number of sampling times and the number of iterations on detection accuracy and inference speed. The DiffusionDet model effectively solves the problem of speckle noise and small target detection in wide-band SAR image target detection, and provides a new method for SAR image target detection.