Citation: Yu, J.; Wu, T.; Zhou, S.; Pan,
H.; Zhang, X.; Zhang, W. SAR Ship
Object Detection Algorithm Based on
Feature Information Efficient
Representation Network. Remote
Sens. 2022, 14, 3489. https://doi.org/
10.3390/rs14143489
Academic Editor: Yangquan Chen
Received: 27 June 2022
Accepted: 16 July 2022
Published: 21 July 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An SAR Ship Object Detection Algorithm Based on Feature
Information Efficient Representation Network
Jimin Yu
1
, Tao Wu
1,
* , Shangbo Zhou
2
, Huilan Pan
3
, Xin Zhang
1
and Wei Zhang
1
1
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
yujm@cqupt.edu.cn (J.Y.); S200331117@stu.cqupt.edu.cn (X.Z.); S190301072@stu.cqupt.edu.cn (W.Z.)
2
College of Computer Science, Chongqing University, Chongqing 400044, China; shbzhou@cqu.edu.cn
3
School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
panhl@cqu.edu.cn
* Correspondence: S200303029@stu.cqupt.edu.cn
Abstract:
In the synthetic aperture radar (SAR) ship image, the target size is small and dense, the
background is complex and changeable, the ship target is difficult to distinguish from the surrounding
background, and there are many ship-like targets in the image. This makes it difficult for deep-
learning-based target detection algorithms to obtain effective feature information, resulting in missed
and false detection. The effective expression of the feature information of the target to be detected
is the key to the target detection algorithm. How to improve the clear expression of image feature
information in the network has always been a difficult point. Aiming at the above problems, this paper
proposes a new target detection algorithm, the feature information efficient representation network
(FIERNet). The algorithm can extract better feature details, enhance network feature fusion and
information expression, and improve model detection capabilities. First, the convolution transformer
feature extraction (CTFE) module is proposed, and a convolution transformer feature extraction
network (CTFENet) is built with this module as a feature extraction block. The network enables the
model to obtain more accurate and comprehensive feature information, weakens the interference of
invalid information, and improves the overall performance of the network. Second, a new effective
feature information fusion (EFIF) module is proposed to enhance the transfer and fusion of the main
information of feature maps. Finally, a new frame-decoding formula is proposed to further improve
the coincidence between the predicted frame and the target frame and obtain more accurate picture
information. Experiments show that the method achieves 94.14% and 92.01% mean precision (mAP)
on SSDD and SAR-ship datasets, and it works well on large-scale SAR ship images. In addition,
FIERNet greatly reduces the occurrence of missed detection and false detection in SAR ship detection.
Compared to other state-of-the-art object detection algorithms, FIERNet outperforms them on various
performance metrics on SAR images.
Keywords:
FIERNet; CTFE; CTFENet; EFIF module; bounding box regression decoding; SAR ship
detection
1. Introduction
In recent years, target detection technology based on deep learning has made great
breakthroughs in detection performance, gradually replacing traditional methods, and
is widely used in autonomous driving [
1
,
2
], face recognition [
3
,
4
], remote sensing object
detection [
5
,
6
], pose detection [
7
,
8
], and many other fields. Among them, the target
detection algorithm application of deep learning in synthetic aperture radar (SAR) ship
detection has received extensive attention. Object detection methods are generally divided
into two-stage detection and single-stage detection. The two-stage detection first generates
a preselected box through the proposal region network, and then the detection network
realizes the classification and regression of the preselected box, so it has a high target
Remote Sens. 2022, 14, 3489. https://doi.org/10.3390/rs14143489 https://www.mdpi.com/journal/remotesensing
