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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 97-104     DOI: 10.6046/zrzyyg.2021130
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High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4
SUN Yu1(), HUANG Liang1,2(), ZHAO Junsan1,3,4, CHANG Jun5, CHEN Pengdi1, CHENG Feifei1
1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China
3. Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
4. Spatial Information Integration Technology of Natural Resources in Universities of Yunnan Province, Kunming 650211, China
5. The First Geodetic Surveying Brigade of Ministry of Natural Resources, Xi’an 710054, China
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Abstract  

As a typical and important ground target, the bridge is the vital passage between transportation lines, so automatic detection of a bridge is of great social and economic significance. Deep learning has become a new way of bridge detection, but the detection accuracy for bridges obscured by cloud and mist is low. In order to solve this problem, an automatic bridge target detection method combining Random erase (RE) data enhancement and the YOLOv4 model is proposed: firstly, the scale range of the target in the data set is determined, and the candidate frame size is obtained by K-means clustering; secondly, the cloud obscuration is simulated by a combination of RE and mosaic data enhancement; thirdly, the enhanced data set is trained by YOLOv4 network; and finally, the mean Average Precision (mAP) is used to evaluate the experimental results. The experimental results show that the detection accuracy obtained by mAP is 97.06%, which is 2.99% higher than that of traditional YOLOv4, and the average detection accuracy of bridges obscured by a cloud is improved by 12%, which verifies the effectiveness and practicability of the proposed method.
Keywords deep learning      object detection      high spatial resolution remote sensing image      bridge detection      YOLOv4 network     
ZTFLH:  P236  
Corresponding Authors: HUANG Liang     E-mail: sunyu_kust@163.com;kmhuangliang@163.com
Issue Date: 20 June 2022
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Yu SUN
Liang HUANG
Junsan ZHAO
Jun CHANG
Pengdi CHEN
Feifei CHENG
Cite this article:   
Yu SUN,Liang HUANG,Junsan ZHAO, et al. High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4[J]. Remote Sensing for Natural Resources, 2022, 34(2): 97-104.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021130     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/97
Fig.1  Flow chart of proposed method
Fig.2  Sample data set
序号 1 2 3 4 5 6 7 8 9
x 19 24 28 36 55 64 108 116 200
y 16 76 34 173 25 56 382 37 129
Tab.1  The results of k-means
Fig.3  The flow chart of random erase data augmentation
Fig.4  Schematic diagram of CIoU
模型 网络 +RE 涨点 本文涨点
SSD 61.09 66.82 5.73 35.97
Mobilenet-SSD 66.56 70.23 3.67 30.50
Centernet 82.07 84.28 2.21 14.99
Efficientdet 76.67 76.44 1.77 20.39
Retinanet 78.23 80.12 1.89 18.83
YOLOv3 93.82 95.66 1.84 3.24
YOLOv4 94.07 97.06 2.99 2.99
Tab.2  The detection results of different models
Fig.5  The detection results of YOLOv3
Fig.6  The detection results of YOLOv4
Fig.7  The detection results of proposed method
模型 常规 复杂
背景		 多尺度 并行 云雾
遮挡
共含桥梁/个 5 3 19 6 8
正确检出/个 YOLOv3 4 1 18 4 3
YOLOv4 5 2 18 5 4
本文方法 5 3 19 5 8
平均检测准确度/% YOLOv3 98.25 89.00 95.81 98.00 93.33
YOLOv4 94.20 88.00 99.70 97.40 88.00
本文方法 99.80 99.50 99.59 100.00 100.00
Tab.3  Sample test results
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