Paper Info
Title
YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module
Abstract
As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.
Year
DOI
Venue
2022
10.3390/rs14205268
REMOTE SENSING
Keywords
DocType
Volume
synthetic aperture radar (SAR), small ship detection, deep learning, YOLOX
Journal
14
Issue
ISSN
Citations 
20
2072-4292
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Simin Wang100.34
Song Gao293.90
Lun Zhou300.34
Ruochen Liu400.34
Hengsheng Zhang500.34
Jiaming Liu600.34
Yong Jia700.34
Jiang Qian800.68