CCDN-DETR:一种基于受约束对比度否定的检测变压器,用于多类合成孔径雷达对象检测
Lei Zhang1, Jiachun Zheng1, Chaopeng Li1
1School of Ocean Information Engineering, Jimei University, Xiamen 361021, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究介绍了CCDN-DETR,这是一种新的合成孔径雷达 (SAR) 对象检测模型. 它通过利用变压器架构显著提高了船舶目标识别和多类检测准确度.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 卷积神经网络 (CNN) 是有效的SAR对象检测,特别是船舶目标.
- 将变压器结构集成到SAR探测器中可以增强目标定位,但现有的方法无法充分利用自我注意的远程建模.
- 多类SAR目标检测仍然是一个研究有限的领域.
研究的目的:
- 提出一种基于检测变压器 (DETR) 框架的新型SAR探测器CCDN-DETR.
- 解决现有的SAR探测器的局限性,包括充分利用自我注意力和改进多类检测.
- 为了适应基于变压器的探测器对SAR数据的多尺度特征.
主要方法:
- 开发了CCDN-DETR,这是一个基于检测变压器 (DETR) 框架的SAR检测器.
- 引入交叉尺度编码器,以在SAR数据中模拟和融合不同尺度的信息.
- 优化解码器输入,采用IOU损失对象查询初始化,并结合受约束的对比性拒绝训练.
主要成果:
- 在SSDD,HRSID和SAR-AIRcraft数据集的组合中,CCDN-DETR实现了91.9%的平均平均精度 (mAP).
- 在多类MSAR数据集上表现出强的表现,mAP为83.7%,优于基于CNN的模型.
- 提出的方法提高了模型的融合速度,并改善了各种SAR目标类别的检测.
结论:
- CCDN-DETR有效地利用变压器架构来改进SAR对象检测,特别是在多类场景中.
- 跨尺度编码器和优化查询选择方案的集成解决了SAR数据的多尺度性质.
- 这项研究通过提供更强大,更准确的检测模型来推进SAR目标识别.
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