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An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys

文献类型：期刊论文

作者Wu, Chengbin2,3; Huang, Yaohuan2,3; Yang, Haijun1; Yao, Ling2,3; Liu, Yesen4; Chen, Zhuo2,3
刊名INTERNATIONAL JOURNAL OF DIGITAL EARTH
出版日期2024-12-31
卷号17期号:1页码:2390443
关键词Deep learning object detection dataset outfalls UAS imagery computer vision
DOI10.1080/17538947.2024.2390443
产权排序1
文献子类Article
英文摘要Deep-learning-based object detection in UAS imagery is crucial for outfall surveys and basin environmental protection. Comprehensive UAS image datasets serve as a foundation for creating deep-learning-based outfall-detection models and evaluating outfall-detection algorithms. However, existing remote sensing deep learning datasets lack specific outfall data. This study introduces a benchmark UAS image dataset of outfalls, categorized into three main and seven subcategories. Over 10,000 labeled images were collected from UAS images with a 10 cm resolution for the Yangtze River, Yellow River, and other basins from 2019 to 2022, covering nearly all types of outfalls in China. Each sample was matched with digital surface model (DSM) data generated through photographometry or the DSM transfer method. Evaluation with seven widely used deep learning-based object detection algorithms demonstrated the dataset's viability, achieving an average precision ($AP_{50}$AP50) of 64.6, surpassing performance on Microsoft Common Objects in Context. Further experiments indicated that DSMs attached to this dataset could benefit from geo-deep-learning-based object detection algorithms for outfalls, achieving an $AP_{50}$AP50 exceeding 70. This study presents a UAS benchmark image dataset for outfall surveys, potentially advancing the application of deep learning in environmental protection.
WOS关键词REMOTE-SENSING IMAGES ; AUTOMATIC DETECTION ; VEHICLE DETECTION ; TARGET DETECTION
WOS研究方向Physical Geography ; Remote Sensing
WOS记录号WOS:001290811900001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/206861]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Huang, Yaohuan
作者单位1.Minist Ecol & Environm, Ctr Satellite Applicat Ecol & Environm, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China
4.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Chengbin,Huang, Yaohuan,Yang, Haijun,et al. An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):2390443.
APA Wu, Chengbin,Huang, Yaohuan,Yang, Haijun,Yao, Ling,Liu, Yesen,&Chen, Zhuo.(2024).An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),2390443.
MLA Wu, Chengbin,et al."An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):2390443.

入库方式： OAI收割

来源：地理科学与资源研究所

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