For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules. The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once (Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision (mAP) indicators on the mini-RD and SAR ship detection dataset (SSDD) reach 83.21% and 85.46%, respectively, which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.

中文翻译：

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Lira-YOLO：一种用于雷达图像中船舶检测的轻量级模型

对于雷达图像中海洋船舶物体的检测，基于深度学习的大规模网络难以部署在现有的配备雷达的设备上。本文提出了一种轻量级卷积神经网络LiraNet，它结合了密集连接、残差连接和组卷积的思想，包括stem blocks和extractor模块。设计的stem block使用一系列小卷积来提取输入图像特征，提取器网络采用设计的双向密集连接模块，进一步降低了网络操作的复杂度。在目标检测框架 Darknet 上安装 LiraNet，本文提出了 Lira-you only look once (Lira-YOLO)，这是一种用于雷达图像中船舶检测的轻量级模型，可以轻松部署在移动设备上。里拉-YOLO' s 预测模块使用了两层 YOLO 预测层，并添加了残差模块以获得更好的特征传递。同时，为了充分验证模型的性能，构建了轻量级距离多普勒域雷达图像数据集mini-RD。实验表明，Lira-YOLO的网络复杂度低，仅为2.980 Bflops，参数量更小，仅为4.3 MB。mini-RD和SAR船舶检测数据集（SSDD）上的平均精度（mAP）指标分别达到83.21%和85.46%，与tiny-YOLOv3相当。Lira-YOLO 以较少的内存和计算成本实现了良好的检测精度。为了充分验证模型的性能，构建了轻量级距离多普勒域雷达图像数据集mini-RD。实验表明，Lira-YOLO的网络复杂度低，仅为2.980 Bflops，参数量更小，仅为4.3 MB。mini-RD和SAR船舶检测数据集（SSDD）上的平均精度（mAP）指标分别达到83.21%和85.46%，与tiny-YOLOv3相当。Lira-YOLO 以较少的内存和计算成本实现了良好的检测精度。为了充分验证模型的性能，构建了轻量级距离多普勒域雷达图像数据集mini-RD。实验表明，Lira-YOLO的网络复杂度低，仅为2.980 Bflops，参数量更小，仅为4.3 MB。mini-RD和SAR船舶检测数据集（SSDD）上的平均精度（mAP）指标分别达到83.21%和85.46%，与tiny-YOLOv3相当。Lira-YOLO 以较少的内存和计算成本实现了良好的检测精度。mini-RD和SAR船舶检测数据集（SSDD）上的平均精度（mAP）指标分别达到83.21%和85.46%，与tiny-YOLOv3相当。Lira-YOLO 以较少的内存和计算成本实现了良好的检测精度。mini-RD和SAR船舶检测数据集（SSDD）上的平均精度（mAP）指标分别达到83.21%和85.46%，与tiny-YOLOv3相当。Lira-YOLO 以较少的内存和计算成本实现了良好的检测精度。