An Efficient Center-Based Method With Multilevel Auxiliary Supervision for Multiscale SAR Ship Detection

The problem of multiscale ship detection in synthetic aperture radar (SAR) images has received much attention with the development of deep convolutional neural networks (DCNNs). However, existing DCNN-based multiscale SAR ship detection methods often lead to time-consuming detection process due to the massive parameters therein. To address this issue, a lightweight center-based detector with the multilevel auxiliary supervision (MLAS) structure is proposed in this article. First, an extremely lightweight backbone network is designed to improve the computation efficiency and extract SAR image features in a bottom-up manner. Then, a feature fusion network containing three multiscale feature fusion modules is introduced to combine semantic features with different levels. Finally, a novel MLAS-based framework is proposed to train our DCNN with multilevel auxiliary detection subnets. MLAS improves the performance of multiscale ship detection benefiting from the guidance of multilevel attention. Experimental results on the open SAR image dataset SSDD show that our proposed detector achieves a similar average precision for the problem of multiscale SAR ship detection but significantly reduces the computation burden of state-of-the-art methods. The required number of floating points of operations of our method is only 21.70%, 19.30%, and 4.81% of those of CenterNet, YOLOv3, and RetinaNet, respectively, and the number of learnable weights in our method is only 0.68 million that is 5.63%, 1.10%, 2.98% of those of the aforementioned three existing methods, respectively.