Wang Jielei, Cui Zongyong, Jiang Ting, Cao Changjie, Cao Zongjie
IEEE Trans Image Process. 2023;32:565-579. doi: 10.1109/TIP.2022.3231126. Epub 2023 Jan 4.
In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.
近年来，深度卷积神经网络（DCNNs）已广泛应用于合成孔径雷达（SAR）图像中的舰船目标检测任务。然而，DCNN巨大的存储和计算成本限制了其在资源有限的星载或机载设备上的应用。本文提出了一组用于SAR舰船目标检测的轻量级检测网络。为了获得这些轻量级网络，本文设计了一种基于多目标萤火虫算法（称为NOFA）的网络结构优化算法。在我们的设计中，NOFA算法将性能良好的舰船目标检测网络的滤波器编码为一个概率列表，该列表将决定轻量级网络是否会继承相应的滤波器结构和参数。之后，多目标萤火虫优化算法（MFA）不断优化概率列表，最终输出一组能够满足检测网络精度和规模之间不同权衡需求的轻量级网络编码。最后，网络剪枝技术将满足任务要求的编码转换为轻量级舰船目标检测网络。在SSDD和SDCD数据集上的实验证明，本文提出的方法能够提供比传统检测网络更灵活、更轻量级的检测网络。
