Comparative analysis of validating parameters in the deep learning models for remotely sensed images

IF 1.2 Q2 MATHEMATICS, APPLIED
Ravi Kumar, Deepak Kumar
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引用次数: 1

Abstract

Abstract The recognition of object in remotely sensed images is a complex task. The immense research is running in the field of remote sensing due to the availability of high resolution satellite images. The detection of object is a challenging task due to the complex background and small object size in remotely sensed images. The object detection in remote sensing images has a vital role in the field of navigation, salvage, and military. The performance of traditional algorithms is very less due to the usage of handcrafted features. With the initiation of Deep Learning algorithms, various Convolutional Neural Networks (CNN) based model have been utilized to detect the objects with high-resolution remotely sensed images. In this research paper various CNN based models has been compared and analyzed. Object detection approaches are broadly categorized in two ways-one based on the region matching and second based on the one-stage target detection. The researchers have compared the result of R-CNN, SPP Net , fast R-CNN, faster R-CNN, R-FCN, Mask R-CNN SSD (Single Shot Multibox Detector), DSSD (Deconvolution Single Shot Multibox Detector), FSSD , YOLO v1,YOLO v2, YOLO v3, Gaussian YOLO v3, RetinaNet which conclude that the minimal average precision for the region based category is best shown by Mask R-CNN with 39.8 mAP in the COCO parameter test and for the one stage detector YOLO v3 shows the best case for the COCO parameter test with 69.1 mAP. In the second phase of the review the researchers found that in comparison to the region based and one stage detector the YOLO v3 model from one stage detector shows the best detection precision percentage with the highest 87% in identifying the object called ship.
遥感图像深度学习模型验证参数的对比分析
遥感图像中目标的识别是一项复杂的任务。由于高分辨率卫星图像的可用性,遥感领域正在进行大量的研究。由于遥感图像背景复杂、目标尺寸小,目标的检测是一项具有挑战性的任务。遥感图像中的目标检测在导航、救助、军事等领域具有重要作用。由于使用手工特征,传统算法的性能非常低。随着深度学习算法的兴起,各种基于卷积神经网络(CNN)的模型被用于高分辨率遥感图像的目标检测。本文对各种基于CNN的模型进行了比较和分析。目标检测方法大致分为两种,一种是基于区域匹配的目标检测方法,另一种是基于单阶段目标检测方法。研究人员比较了R-CNN、SPP Net、快速R-CNN、更快R-CNN、R-FCN、Mask R-CNN SSD(单镜头多盒检测器)、DSSD(反卷积单镜头多盒检测器)、FSSD、YOLO v1、YOLO v2、YOLO v3、高斯YOLO v3、retanet得出结论,基于区域的类别的最小平均精度由Mask R-CNN在COCO参数测试中以39.8 mAP表现最佳,对于一级检测器YOLO v3在COCO参数测试中以69.1 mAP表现最佳。在审查的第二阶段,研究人员发现,与基于区域和一级检测器的YOLO v3模型相比,一级检测器的YOLO v3模型在识别被称为船的物体时显示出最好的检测精度百分比,最高为87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
21.40%
发文量
126
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