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Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet
المؤلفون المشاركون
Han, Xu
Zhao, Lining
Li, Zhelin
Pan, Mingyang
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-28
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Ship detection is one of the most important research contents of ship intelligent navigation and monitoring.
As a supplement to classical navigational equipment such as radar and the Automatic Identification System (AIS), target detection based on computer vision and deep learning has become a new important method.
A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection.
However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications.
On the basis of YOLOv3, this paper proposes a lightweight ship detection model (LSDM) in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace normal convolution.
The two improvements reduce parameters and optimize the network structure greatly.
The experimental results show that, with only one-third of parameters of YOLOv3, the LSDM has higher accuracy and speed for ship detection.
In addition, the LSDM is simplified further by reducing the number of densely connected units to form a model called LSDM-tiny.
The experimental results show that, LSDM-tiny has similar detection speed with YOLOv3-tiny, but has a lot higher accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Zhelin& Zhao, Lining& Han, Xu& Pan, Mingyang. 2020. Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1195461
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Zhelin…[et al.]. Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1195461
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Zhelin& Zhao, Lining& Han, Xu& Pan, Mingyang. Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1195461
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1195461
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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