摘要
针对内河港口背景复杂、类间尺度差异大和小目标实例多的特点,提出基于多头自注意力机制(MHSA)和YOLO网络的船舶目标检测算法(MHSA-YOLO).在特征提取过程中,基于MHSA设计并行的自注意力残差模块(PARM),以弱化复杂背景信息干扰并强化船舶目标特征信息;在特征融合过程中,开发简化的双向特征金字塔结构,以强化特征信息的融合与表征能力.在Seaships数据集上的实验结果表明,与其他先进的目标检测方法相比,MHSA-YOLO拥有较好的学习能力,在检测精度方面取得97.59%的平均均值精度,MHSA-YOLO对复杂背景船舶目标和小尺寸目标的检测更有效.基于自制数据集的实验结果表明,MHSA-YOLO的泛化能力强.
A ship object detection algorithm was proposed based on a multi-head self-attention(MHSA)mechanism and YOLO network(MHSA-YOLO),aiming at the characteristics of complex backgrounds,large differences in scale between classes and many small objects in inland rivers and ports.In the feature extraction process,a parallel self-attention residual module(PARM)based on MHSA was designed to weaken the interference of complex background information and strengthen the feature information of the ship objects.In the feature fusion process,a simplified two-way feature pyramid was developed so as to strengthen the feature fusion and representation ability.Experimental results on the Seaships dataset showed that the MHSA-YOLO method had a better learning ability,achieved 97.59%mean average precision in the aspect of object detection and was more effective compared with the state-of-the-art object detection methods.Experimental results based on a self-made dataset showed that MHSAYOLO had strong generalization.
作者
于楠晶
范晓飚
邓天民
冒国韬
YU Nan-jing;FAN Xiao-biao;DENG Tian-min;MAO Guo-tao(School of Shipping and Naval Architecture,Chongqing Jiaotong University,Chongqing 400074,China;College of Traffic and Transportation,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第12期2392-2402,共11页
Journal of Zhejiang University：Engineering Science
基金
国家重点研发计划项目(SQ2020YFF0418521)
重庆市技术创新与应用发展专项重点项目(cstc2020jscx-dxwtBX0019)
川渝联合实施重点研发项目(cstc2020jscx-cylhX0005,cstc2020jscx-cylhX0007)。
关键词
智能航行
目标检测
复杂背景
自注意力机制
多尺度特征融合
intelligent navigation
object detection
complex background
self-attention mechanism
multiscale fusion
作者简介
于楠晶(1998-),女,硕士生,从事目标检测研究.orcid.org/0000-0001-7617-4478.E-mail:yunanjing527@163.com;通信联系人:邓天民,男,副教授.orcid.org/0000-0003-0511-0519.E-mail:dtianmin@cqjtu.edu.cn。
