[1]梁礼明,冯耀,龙鹏威,等.基于MobileViT和多尺度特征聚合的遥感图像目标检测[J].智能系统学报,2024,19(5):1168-1177.[doi:10.11992/tis.202310022]
LIANG Liming,FENG Yao,LONG Pengwei,et al.Remote sensing image object detection based on MobileViT and multiscale feature aggregation[J].CAAI Transactions on Intelligent Systems,2024,19(5):1168-1177.[doi:10.11992/tis.202310022]
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LIANG Liming,FENG Yao,LONG Pengwei,et al.Remote sensing image object detection based on MobileViT and multiscale feature aggregation[J].CAAI Transactions on Intelligent Systems,2024,19(5):1168-1177.[doi:10.11992/tis.202310022]
基于MobileViT和多尺度特征聚合的遥感图像目标检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1168-1177
栏目:
学术论文—机器感知与模式识别
出版日期:
2024-09-05
- Title:
- Remote sensing image object detection based on MobileViT and multiscale feature aggregation
- 关键词:
- 深度学习; 遥感图像; 目标检测; YOLOv7-tiny; MobileViT模块; 多尺度特征融合; 上下文信息; Wise-IoU
- Keywords:
- deep learning; remote sensing image; object detection; YOLOv7-tiny; MobileViT module; multi-scale feature fusion; contextual information; Wise-IoU
- 分类号:
- TP391
- 文献标志码:
- 2024-08-28
- 摘要:
- 针对遥感图像目标检测存在复杂背景干扰、微小目标提取难和目标多尺度差异问题，提出一种基于MobileViT和多尺度特征聚合的遥感图像目标检测算法(FWM-YOLOv7t)。首先设计多尺度特征聚合模块，建立遥感目标上下文依赖关系，提升多尺度目标和小目标检测精度；然后利用MobileViT模块，融合卷积神经网络和视觉Transformer优点，有效编码局部和全局信息，抑制非目标噪声干扰；最后引入Wise-IoU损失函数，重点关注普通质量锚框，提高算法检测性能。在公共数据集RSOD和NWPU VHR-10上的实验结果表明，FWM-YOLOv7t能够显著提升遥感图像目标检测的平均准确率。与其他目标检测算法相比，FWM-YOLOv7t对复杂背景目标、小目标和多尺度目标的检测更有效。
- Abstract:
- A new algorithm is proposed based on MobileViT and multi-scale feature aggregation (referred to as FWM-YOLOv7t) to address problems such as complex background interference, difficulty in extracting small objects, and object multi-scale differences in remote sensing image object detection. First, we design a multi-scale feature aggregation module to establish context dependencies for remote sensing targets, which improves the accuracy of detecting multi-scale and small targets. Then, we utilize the MobileViT module to fuse the advantages of convolutional neural networks and vision transformers for effective local and global information encoding to suppress non-target noise interference. Finally, we introduce the Wise-IoU loss function, which focuses on ordinary quality anchor boxes to enhance the detection performance of the algorithm. Experimental evaluations on the public RSOD and NWPU VHR-10 dataset demonstrate that FWM-YOLOv7t can significantly improve the average accuracy of remote sensing image target detection. Furthermore, compared with other object detection algorithms, the FWM-YOLOv7t algorithm exhibits superior effectiveness in detecting complex, small, and multiscale objects in remote sensing imagery.
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备注/Memo
收稿日期:2023-10-17。
基金项目:国家自然科学基金项目(51365017，61463018)；江西省自然科学基金面上项目(20192BAB205084)；江西省教育厅科学技术研究重点项目(GJJ170491).
作者简介:梁礼明，教授，主要研究方向为机器学习、医学影像和系统建模。获得专利授权6项，发表学术论文100余篇，出版专著1部。E-mail：lianglm67@163.com;冯耀，硕士研究生，主要研究方向为深度学习与目标检测。E-mail：fybrave@126.com;龙鹏威，硕士研究生，主要研究方向为机器学习、模式识别与图像处理。E-mail：2637018663@qq.com。
通讯作者:梁礼明. E-mail：lianglm67@163.com
更新日期/Last Update:
2024-09-05