Lira-YOLO: a lightweight model for ship detection in radar images

• 作者: CUI Zhongma DING Wei FANG Jiaqi WEI Suyuan YANG Xiaoting ZHOU Long
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: DEFENCE ELECTRONICS TECHNOLOGY
• 页码范围: 950-956
• 页数: 7页
• 语种: 英文
• DOI: 10.23919/JSEE.2020.000063

摘要

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.