{"title":"Application of Improved YOLO V5s Model for Regional Poverty Assessment Using Remote Sensing Image Target Detection","authors":"Zhang Chenguang, Teng Guifa","doi":"10.14358/pers.23-00005r3","DOIUrl":null,"url":null,"abstract":"This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sensing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the application of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inexpensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.23-00005r3","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sensing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the application of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inexpensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty.
本研究旨在将改进的You Only Look Once V5s模型应用于基于遥感图像目标检测的区域贫困评估。从结构、算法和组件三个方面对模型进行了改进。利用遥感影像中的地物对贫困进行识别,根据已有的检测结果对扶贫情况进行预测。结果表明,在检测阶段,该模型的Precision、Recall、mean Average Precision (mAP)@0.5和mAP@0.5:0.95分别比Common Objects in Context数据集提高了7.3%、0.7%、1%和7.2%;在验证阶段的自定义遥感图像数据集上,这四个值分别增长3.1%、2.2%、1.3%和5.7%。在两个数据集上,损失值分别下降2.6%和37.4%。因此,改进模型的应用使得目标的检测更加准确。与其他文献相比,本文改进的模型效果更好。人工扶贫可以被遥感图像处理取代,因为它便宜、高效、准确、客观、不需要数据,而且具有同样的评价效果。所提出的模型可以被认为是评估区域贫困的一种很有前途的方法。
期刊介绍:
Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers.
We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.