Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images

Shanwei Liu,Limin Zhao,Weimin Kong,Xingfeng Chen,Jiaguo Li,Mingming Xu,Muhammad Yasir

doi:10.3390/rs14051149

Abstract

The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network.

Highlights

Synthetic aperture radar (SAR), an essential aerospace remote sensor, is characterized by its ability to achieve all-weather observation of the ground [1]
(2) Under the same software and hardware environment and dataset, the applicability of the improved network in the task of satellite-borne ship target detection is verified according to comparing the detection performance of YOLOv3, SSD, YOLOv4, MobileNet-YOLOv4, and the improved
In order to verify the effect of adding receptive field block (RFB) module and improving RFB module on the accuracy of the algorithm and the optimization of depth-separable convolution on the number of parameters, a set of comparison experiments were set up based on the MobileNet-YOLOv4-LITE network to compare and analyze the effect of replacing only the backbone feature extraction network model, the depth-separable convolution replacement model, the original RFB structure, and the improved RFB structure trained with SSDD

Summary

Introduction

Synthetic aperture radar (SAR), an essential aerospace remote sensor, is characterized by its ability to achieve all-weather observation of the ground [1]. Based on this advantage, SAR technology has developed rapidly [2–4], and has a wide range of applications in target detection [5], disaster detection, military operations [6], and resource exploration [7]. As the maritime trade and transportation carrier is an important military object, it is of great significance to realize the real-time detection of ship targets in spaceborne SAR images [9]. The methods of ship target detection [11] in SAR images are mainly divided into two types: traditional detection methods, and target detection methods based on deep learning [12]

Methods

Results

Conclusion