YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm

Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship target detection models. These methods include Constant False Alarm Rate (CF...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 16; H. 5; S. 913
Hauptverfasser:	Liu, Yilin, Ma, Yong, Chen, Fu, Shang, Erping, Yao, Wutao, Zhang, Shuyan, Yang, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.03.2024
Schlagworte:	Accuracy Algorithms Artificial satellites in remote sensing Boxes Constant false alarm rate data collection Datasets Deep learning Image processing Localization Machine learning Methods oriented bounding box Radar imaging Remote sensing ship detection Ships Synthetic aperture radar Synthetic aperture radar (SAR) images Target detection YOLO algorithm China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!