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Ship detection algorithm in complex backgrounds via multi-head self-attention |
Nan-jing YU1( ),Xiao-biao FAN1,Tian-min DENG2,*( ),Guo-tao MAO2 |
1. School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, China 2. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China |
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Abstract 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 MHSA-YOLO had strong generalization.
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Received: 11 January 2022
Published: 03 January 2023
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Fund: 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) |
Corresponding Authors:
Tian-min DENG
E-mail: yunanjing527@163.com;dtianmin@cqjtu.edu.cn
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基于多头自注意力的复杂背景船舶检测算法
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.
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
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