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作 者:龙翔 陈华杰[1] 吴浩宇 余迪 Long Xiang;Chen Huajie;Wu Haoyu;Yu Di(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,Zhejiang,China)
机构地区:[1]杭州电子科技大学自动化学院,浙江杭州310018
出 处:《激光与光电子学进展》2025年第2期156-167,共12页Laser & Optoelectronics Progress
基 金:浙江省科技计划(2022C01095)。
摘 要:针对海上搜救行动中海面目标检测任务面临的高动态背景干扰、多尺度目标检测、小目标特征信息易丢失等问题,基于特征增强、自适应特征融合的思想,以YOLOv8网络为基线模型,提出一种改进的TS-YOLOv8网络。首先,基于Transformer中的查询机制设计TFF(Transformer-based feature fusion)模块,通过不同尺度特征之间的深度信息交互,对各特征层实现特征增强,再利用可学习参数对各层特征进行自适应特征融合。其次,引入几乎无参数的Shuffle Attention注意力机制,在保持网络轻量化的同时捕捉更多复杂的特征信息。在AFO数据集上进行所提模型与多种主流检测算法的对比实验和多组消融实验,相较于基线模型,所提方法的mAP50提高5.60百分点,达到95.14%,mAP95提高7.38百分点,检测速度达到110 frame/s。在SeaDronesSee数据集上进行多组消融实验,相较于基线模型,所提方法的mAP50提高4.47百分点,达到91.34%,mAP95提高5.92百分点,检测速度达到106 frame/s。以上结果表明,所提模型可充分满足海上搜救任务的苛刻要求。To address the challenges posed by high dynamic background interference,multi-scale target detection,and potential feature loss in sea surface target detection tasks during maritime search and rescue operations,based on the YOLOv8 model,this study proposes an improved TS-YOLOv8 network based on the ideas of feature augmentation and adaptive feature fusion.First,a Transformer-based feature fusion(TFF)module is introbuced based on the Transformer’s query mechanism.This module facilitates feature augmentation across various scales by enabling depth information interaction among different feature layers.Second,employing learnable parameters,the network adaptively fuses features from each layer.Third,this paper integrates an almost parameter-free Shuffle Attention mechanism to capture intricate feature details while ensuring network efficiency.Comparison experiments with a variety of mainstream detection algorithms and multiple sets of ablation experiments are carried out on the AFO dataset,the mAP50 of the proposed method reaches 95.14%.Compared with the baseline model,the mAP50 is increased by 5.60 percentage points,the mAP95 is increased by 7.38 percentage points,and the FPS reaches 110 frames/s.Multiple sets of ablation experiments are carried out on the SeaDronesSee dataset,the mAP50 of the proposed method reaches 91.34%.Compared with the baseline model,the mAP50 is increased by 4.47 percentage points,the mAP95 is increased by 5.92 percentage points,and the FPS reaches 106 frames/s.Results indicate that proposed model can fully meet the demanding requirements of maritime search and rescue missions.
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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