基于集中式特征金字塔的监督深度学习,从 GPR 数据中建立物体检测模型

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Kun Yan, Xianlei Xu, Pengqiao Zhu, Zhaoyang Zhang
{"title":"基于集中式特征金字塔的监督深度学习,从 GPR 数据中建立物体检测模型","authors":"Kun Yan,&nbsp;Xianlei Xu,&nbsp;Pengqiao Zhu,&nbsp;Zhaoyang Zhang","doi":"10.1111/1365-2478.13590","DOIUrl":null,"url":null,"abstract":"<p>To address low detection accuracy and speed due to the multisolvability of the ground-penetrating radar signal, we proposed a novel centralized feature pyramid-YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra-layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground-penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three-dimensional imaging. Our model is suitable for object detection in ground-penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Centralized feature pyramid-based supervised deep learning for object detection model from GPR data\",\"authors\":\"Kun Yan,&nbsp;Xianlei Xu,&nbsp;Pengqiao Zhu,&nbsp;Zhaoyang Zhang\",\"doi\":\"10.1111/1365-2478.13590\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To address low detection accuracy and speed due to the multisolvability of the ground-penetrating radar signal, we proposed a novel centralized feature pyramid-YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra-layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground-penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three-dimensional imaging. Our model is suitable for object detection in ground-penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.</p>\",\"PeriodicalId\":12793,\"journal\":{\"name\":\"Geophysical Prospecting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geophysical Prospecting\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.13590\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geophysical Prospecting","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.13590","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0

摘要

针对透地雷达信号的多可变性导致的检测精度和速度较低的问题,我们提出了一种新颖的基于集中特征金字塔-YOLOv6l 的模型,以提高道路损坏和管道检测的检测精度和速度。集中式特征金字塔用于获取丰富的层内特征,提高网络性能。与现有的检测模型相比,我们提出的模型达到了更高的精度。我们还建立了两个新的评估指标:相对平均精度和相对平均精度,以全面评估检测精度。为了验证模型的适用性,我们在收集到的探地雷达数据集上进行了道路现场检测实验,发现所提出的模型在提高检测精度方面有良好的表现,与 YOLOv7、YOLOv5 和 YOLOx 模型相比,平均精度最高,相对平均精度和每秒帧率分别为 16.38% 和 30.5%。道路损坏和管道的检测信息被用于进行三维成像。我们的模型适用于探地雷达图像中的物体检测,从而为道路损坏和地下管道检测提供技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Centralized feature pyramid-based supervised deep learning for object detection model from GPR data

To address low detection accuracy and speed due to the multisolvability of the ground-penetrating radar signal, we proposed a novel centralized feature pyramid-YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra-layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground-penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three-dimensional imaging. Our model is suitable for object detection in ground-penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geophysical Prospecting
Geophysical Prospecting 地学-地球化学与地球物理
CiteScore
4.90
自引率
11.50%
发文量
118
审稿时长
4.5 months
期刊介绍: Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信