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结合随机擦除和YOLOv4的高空间分辨率遥感影像桥梁自动检测

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文章导航 > 自然资源遥感 > 2022 > 34(2): 97-104

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孙宇, 黄亮, 赵俊三, 常军, 陈朋弟, 成飞飞. 2022. 结合随机擦除和YOLOv4的高空间分辨率遥感影像桥梁自动检测. 自然资源遥感, 34(2): 97-104. doi: 10.6046/zrzyyg.2021130
引用本文: 孙宇, 黄亮, 赵俊三, 常军, 陈朋弟, 成飞飞. 2022. 结合随机擦除和YOLOv4的高空间分辨率遥感影像桥梁自动检测. 自然资源遥感, 34(2): 97-104. doi: 10.6046/zrzyyg.2021130
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SUN Yu, HUANG Liang, ZHAO Junsan, CHANG Jun, CHEN Pengdi, CHENG Feifei. 2022. High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4. Remote Sensing for Natural Resources, 34(2): 97-104. doi: 10.6046/zrzyyg.2021130
Citation: SUN Yu, HUANG Liang, ZHAO Junsan, CHANG Jun, CHEN Pengdi, CHENG Feifei. 2022. High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4. Remote Sensing for Natural Resources, 34(2): 97-104. doi: 10.6046/zrzyyg.2021130
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结合随机擦除和YOLOv4的高空间分辨率遥感影像桥梁自动检测

  • 1.昆明理工大学国土资源工程学院,昆明 650093；2.云南省高校高原山区空间信息测绘技术应用工程研究中心,昆明 650093；3.智慧矿山地理空间信息集成创新重点实验室,昆明 650093；4.云南省高校自然资源空间信息集成与应用科技创新团队,昆明 650211；5.自然资源部第一大地测量队,西安 710054

  • 基金项目:

    国家自然科学基金项目”南方山地城镇建设用地与变化的坡度梯度效应研究”(41961039)

    云南省应用基础研究计划面上项目”基于全卷积神经网络的多源遥感影像变化检测”(2018FB078)

    云南省高校工程中心建设计划共同资助

详细信息
    作者简介: 孙 宇(1996-),女,硕士研究生,主要研究方向为遥感影像目标检测。Email: sunyu_kust@163.com。

  • 中图分类号: P236

High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4

  • 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

    摘要
  • 桥梁作为典型且重要的地面目标,是交通运输线路的咽喉,对桥梁进行自动检测具有十分重要的社会和经济意义。深度学习已成为桥梁检测新方式,但对云雾遮挡的桥梁检测精度较低。针对该问题,提出了一种结合随机擦除(random erase, RE)数据增强和YOLOv4模型的桥梁目标自动检测方法,首先统计数据集中目标的尺度范围,利用k-means聚类获得锚框尺寸; 然后通过RE与Mosaic数据增强相结合的方法模拟云雾遮挡的情况; 接着采用YOLOv4网络对经增强后的数据集进行训练; 最后采用平均精度(mean average precision, mAP)评估实验结果。实验结果表明,提出方法的mAP为97.06%,比YOLOv4提高了2.99%,其中被云雾遮挡的桥梁平均检测准确度提高了12%,验证了提出方法的有效性及实用性。
    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.
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