H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and...

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Vydané v:Remote sensing (Basel, Switzerland) Ročník 12; číslo 24; s. 4192
Hlavní autori: Tang, Gang, Liu, Shibo, Fujino, Iwao, Claramunt, Christophe, Wang, Yide, Men, Shaoyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: MDPI 21.12.2020
MDPI AG
Predmet:
accuracy
data collection
detection
Engineering Sciences
exhibitions
extracts
Internet
remote sensing
ship detection
ships
Signal and Image processing
solutions
strength training
YOLOv3
ship detection
remote sensing
YOLOv3
ISSN:2072-4292, 2072-4292
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Abstract Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.
AbstractList Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.
Author Wang, Yide
Tang, Gang
Claramunt, Christophe
Men, Shaoyang
Fujino, Iwao
Liu, Shibo
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  fullname: Men, Shaoyang
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Keywords ship detection
remote sensing
YOLOv3
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Snippet Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel...
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SubjectTerms accuracy
data collection
detection
Engineering Sciences
exhibitions
extracts
Internet
remote sensing
ship detection
ships
Signal and Image processing
solutions
strength training
YOLOv3
Title H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network
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