高分影像密集建筑物Correg-YOLOv3检测方法

doi:10.11947/j.AGCS.2022.20210177

测绘学报 ›› 2022, Vol. 51 ›› Issue (12): 2531-2540.doi: 10.11947/j.AGCS.2022.20210177

高分影像密集建筑物Correg-YOLOv3检测方法

陈占龙^1,2,6, 李双江^1,7, 徐永洋^1,3,6, 徐道柱^4,5, 马超^4,5, 赵军利²

1. 中国地质大学(武汉)地理与信息工程学院, 湖北武汉 430078;
2. 中国地质大学(武汉)地质探测与评估教育部重点实验室, 湖北武汉 430074;
3. 自然资源部城市国土资源监测与仿真重点实验室, 广东深圳 518034;
4. 西安测绘研究所, 陕西西安 710054;
5. 地理信息工程国家重点实验室, 陕西西安 710054;
6. 中国地质大学(武汉)计算机学院, 湖北武汉 430078;
7. 中南电力设计院有限公司, 湖北武汉 430071

收稿日期:2021-04-07 修回日期:2021-10-14 发布日期:2023-01-12
通讯作者: 徐永洋 E-mail:yongyangxu@cug.edu.cn
作者简介:陈占龙(1980-),男,博士,教授,研究方向为空间分析算法、空间推理、地理信息系统软件开发与应用。E-mail:chenzhanlong2005@126.com
基金资助:
国家自然科学基金(41871305);国家重点研发计划(2017YFC0602204);中央高校基本科研业务费专项(CUGQY1945);地质探测与评估教育部重点实验室主任基金和中央高校基本科研业务费(GLAB2019ZR02);自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2020-05-068)

Correg-YOLOv3: a method for dense buildings detection in high-resolution remote sensing images

CHEN Zhanlong^1,2,6, LI Shuangjiang^1,7, XU Yongyang^1,3,6, XU Daozhu^4,5, MA Chao^4,5, ZHAO Junli²

1. School of Geography and Information Engineering, China University of Geoscience, Wuhan 430078, China;
2. Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China;
3. Key Laboratory of Urban Land Resource Monitoring and Simulation, Ministry of Natural Resource, Shenzhen 518034, China;
4. Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China;
5. State Key Laboratory of Geo-Information Engineering, Xi'an 710054, China;
6. School of Computer Science, China University of Geoscience, Wuhan 430078, China;
7. Central Southern China Electric Power Design Institute Co., Ltd., Wuhan 430071, China

Received:2021-04-07 Revised:2021-10-14 Published:2023-01-12
Supported by:
The National Natural Science Foundation of China(No.41871305);The National Key Research and Development Program of China(No.2017YFC0602204);The Fundamental Research Funds for the Central Universities, China University of Geosciences(Wuhan) (No.CUGQY1945);The Open Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education and the Fundamental Research Funds for the Central Universities(No.GLAB2019ZR02);The Open Fund of Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, China (No.KF-2020-05-068)

摘要/Abstract

摘要： 精准地检测建筑物目标对于城市规划、智慧城市建设和军事民事活动中均有重要意义。针对高分辨率遥感影像中密集型建筑物检测框重叠比高的问题,本文提出了一种Correg-YOLOv3(corner regression-based YOLOv3)检测方法,该方法以YOLOv3网络架构为基础,通过嵌入角点回归机制,增设一个关于顶点相对于边界框中心点的偏移量的额外损失项,扩展其输出维度,使其可同时输出矩形检测框及建筑物角点,实现密集分布的建筑物精准定位。最后,通过试验对本文方法进行定性和定量的评估。试验研究结果表明:本文方法检测精度、召回率、F₁和平均精度分别达到了96.45%、95.75%、96.10%和98.05%,较原算法YOLOv3分别提高了2.73%、5.4%、4.1%和4.73%。因此,本文方法有效解决了高分影像中密集型建筑物的检测问题。

关键词: 高分遥感影像, Correg-YOLOv3, 角点回归, 密集建筑物, 目标检测

Abstract: The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high-resolution remote sensing images, we present an effective YOLOv3 framework, corner regression-based YOLOv3 (Correg-YOLOv3), to localize dense building accurately. This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box. By extending output dimensions, the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile. Finally, we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively. The experimental results achieve high performance in precision (96.45%), recall rate (95.75%), F₁ score (96.10%) and average precision (98.05%), which were 2.73%, 5.4%, 4.1% and 4.73% higher than that of YOLOv3. Therefore, the proposed algorithm effectively tackles the problem of dense building detection in high-resolution images.

Key words: high resolution remote sensing image, Correg-YOLOv3, corner regression, dense buildings, object detection

中图分类号:

P237

陈占龙, 李双江, 徐永洋, 徐道柱, 马超, 赵军利. 高分影像密集建筑物Correg-YOLOv3检测方法[J]. 测绘学报, 2022, 51(12): 2531-2540.

CHEN Zhanlong, LI Shuangjiang, XU Yongyang, XU Daozhu, MA Chao, ZHAO Junli. Correg-YOLOv3: a method for dense buildings detection in high-resolution remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(12): 2531-2540.

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高分影像密集建筑物Correg-YOLOv3检测方法

Correg-YOLOv3: a method for dense buildings detection in high-resolution remote sensing images

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