Large-Difference-Scale Target Detection Using a Revised Bhattacharyya Distance in SAR Images
Jiaxin Tang, Jianda Cheng, Deliang Xiang, Canbin Hu
Abstract
Small target detection is a very challenging problem since a small target contains only a few pixels in size. At present, many deep learning-based detection algorithms for small targets have achieved remarkable results, mainly including improvements in data augmentation, multiscale images, multiscale features, training strategies, and so on. However, these deep learning-based methods cannot select the positive and negative samples for the large-difference-scale targets well in the label assignment operation. The reason is that intersection over union (IoU), which is widely used in the most target detection networks, has great limitations for small target detection. However, in practical applications, there are often some large-difference-scale targets in synthetic aperture radar (SAR) images, especially existing some tiny targets due to the limitation of resolution. To fundamentally break the limitations of IoU, we propose to use the Bhattacharyya distance (BD) instead of the IoU metric to improve the performance of small target detection. We further revise the Bhattacharyya distance (RBD) to better measure the deviation of bounding boxes for targets with large differences in size. RBD can embed anchor-based detectors to replace the IoU metric in label assignment and nonmaximum suppression (NMS). The proposed method is evaluated on the LS-SSDD-v1.0 dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.