Paper Info
Title
FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology
Abstract
In the past few years, Synthetic Aperture Radar (SAR) has been widely used to detect marine ships due to its ability to work in various weather conditions. However, due to the imaging mechanism of SAR, there is a lot of background information and noise information similar to ships in the images, which seriously affects the performance of ship detection models. To solve the above problems, this paper proposes a new ship detection model called Feature enhancement and Land burial Net (FLNet), which blends traditional image processing methods with object detection approaches based on deep learning. We first design a SAR image threshold segmentation method, Salient Otsu (S-Otsu), according to the difference between the object and the noise background. To better eliminate noise in SAR images, we further combine image processing methods such as Lee filtering. These constitute a Feature Enhancement Module (FEM) that mitigates the impact of noise data on the overall performance of a ship detection model. To alleviate the influence of land information on ship detection, we design a Land Burial Module (LBM) according to the morphological differences between ships and land areas. Finally, these two modules are added to You Only Look Once V5 (YOLO V5) to form our FLNet. Experimental results on the SAR Ship Detection Dataset (SSDD) dataset show that FLNet comparison with YOLO V5 accuracy when performing object detection is improved by 7% and recall rate by 6.5%.
Year
DOI
Venue
2022
10.3390/rs14194857
REMOTE SENSING
Keywords
DocType
Volume
pixel segmentation, Synthetic Aperture Radar (SAR), ship detection, You Only Look Once (YOLO)
Journal
14
Issue
ISSN
Citations 
19
2072-4292
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Gang Tang102.03
Hongren Zhao200.34
Christophe Claramunt3846107.03
Shaoyang Men401.35