|本期目录/Table of Contents|

[1]杨俊秀,王荣杰,林安辉,等.基于YOLOv5算法的多尺度小目标船舶识别方法[J].集美大学学报(自然科学版),2024,29(4):344-357.
　YANG Junxiu,WANG Rongjie,LIN Anhui,et al.Multi-Scale Small Target Ships Recognition Method Based on YOLOv5 Algorithm[J].Journal of Jimei University,2024,29(4):344-357.
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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:
第29卷
期数:
2024年第4期
页码:
344-357
栏目:
船舶与机械工程
出版日期:
2024-07-28

文章信息/Info

Title:
Multi-Scale Small Target Ships Recognition Method Based on YOLOv5 Algorithm
作者:
杨俊秀1;  王荣杰1; 2;  林安辉1; 2;  王亦春1; 2;  曾广淼1;  蒋德松1; 2
1. 集美大学轮机工程学院，福建 厦门 361021；2.福建省船舶与海洋工程重点实验室，福建 厦门 361021
Author(s):
YANG Junxiu1; WANG Rongjie1; 2; LIN Anhui1; 2; WANG Yichun1; 2; ZENG Guangmiao1; JIANG Desong1; 2
1.School of Marine Engineering,Jimei University,Xiamen 361021,China;2.Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering,Xiamen 361021,China
关键词:
船舶; 多尺度小目标; 图像识别; 数据增强; YOLOv5算法
Keywords:
ship; multi-scale small targets; image recognition; data augmentation; YOLOv5 algorithm
分类号:
-
DOI:
-
文献标志码:
A
摘要:
为提高海面多尺度小目标船舶的识别性能，提出一种数据集划分方法，并在YOLOv5算法中改进数据增强方法，融合注意力机制，改进损失函数。实验结果表明，该方法能更好地识别海面上的多尺度小目标船舶,平均精度（mAP）、精确率（P）、召回率（R）分别为99.1%，98.5%，97.5%，识别性能比经典深度学习算法和近几年的方法都高。
Abstract:
A dataset division method was proposed to improve the recognition performance of multi-scale small target ships on the sea surface. The data augmentation method, which contains the YOLOv5 algorithm,was improved by integrating the attention mechanism and improving the loss function. Experimental results showed that this method could better identify multiscale small target ships on the sea surface,with the average precision (mAP), precision (P) and recall (R) are 99.1%,98.5% and 97.5%,respectively. It suggested that the detection performance was higher than that from classical deep learning algorithms and methods in the past several years.

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备注/Memo

备注/Memo:
更新日期/Last Update: 2024-09-23