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具有边缘信息增强的多尺度特征融合和特征校准,用于远程传感对象检测

Lihua Yang¹, Yi Gu², Hao Feng³

¹School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China. jdzylh@126.com.

Scientific reports

|May 2, 2025

概括

这项研究通过使用改进的EMF-DETR模型来增强高分辨率遥感图像中的对象检测. 该方法提高了准确性,特别是对于小物体,同时降低了计算成本.

科学领域:

计算机视觉计算机视觉
遥感遥感遥感遥感
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

基于视觉变压器的探测器在物体检测方面表现有前途,但由于计算需求和性能瓶,在高分辨率遥感图像方面存在困难.
处理高分辨率图像需要大量的计算资源,特别是捕获细粒度边缘特征,这表明需要性能优化.

研究的目的:

为解决应用基于视觉变压器的探测器对高分辨率遥感图像的计算和性能挑战.
提高对象识别和定位能力,特别是对于小物体,同时降低计算成本.

主要方法:

建议改进一个基于RT-DERT-ResNet-18.8的EMF-DETR模型.
介绍了MEFE-Net,一个多尺度边缘感知特征提取网络,以增强边缘信息和多尺度特征.
包含了CSFCN方法,用于适应性上下文信息调整和精确的空间特征校准.

主要成果:

与基线相比,VisDrone2019数据集的平均平均精度 (mAP) 提高了2.0%.
在小型 (AP_S) 和中型 (AP_M) 物体检测方面分别增加了1.5%和2.6%.
将参数数量减少了20.22%,表明计算成本较低.

结论:

拟议的方法证明了遥感图像分析的检测精度和效率的提高.
增强的特征提取和校准技术为分析高分辨率图像提供了实际应用潜力.
该模型有效地平衡了提高性能和降低计算复杂性的效果.

关键词:

边缘功能增强边缘功能增强功能的校准特征.对象检测检测对象检测对象检测遥感图像遥感图像遥感图像

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