基于深度学习的岩石钻孔全景图像识别
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作者单位:

1.电子科技大学信息与通信工程学院,成都 611731;2.西华大学电气与电子信息学院,成都 610039;3.武汉航工智能科技有限公司,武汉 430100;4.西南科技大学信息与控制工程学院,绵阳 621010;5.西南科技大学特种环境机器人技术四川省重点实验室,绵阳 621010

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国家自然科学基金(62205342);四川省自然科学基金(2025ZNSFSC0522);特种环境机器人技术四川省重点实验室开放基金(22kftk03)。


Panoramic Image Recognition of Rock Borehole Based on Deep Learning
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Affiliation:

1.School of Information and Communication Engineering, University of Electronic Science and Technology of China,Chengdu 611731, China;2.School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China;3.Wuhan Hanggong Intelligent Technology Co., Ltd, Wuhan 430100, China;4.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;5.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang 621010, China

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    摘要:

    岩土钻孔监测作为一种最常见的隧道超前探测技术,可真实、原位反映岩土的材质、特征及地下水情况等,对确保施工安全至关重要。根据岩土钻孔监测目标特点,本文研制了一套基于全景摄像的适用于岩土长孔道内壁近距离、动态高分辨成像的智能视觉系统。通过EfficientNetV2网络的改进和滑动窗口预测,实现了8类岩石钻孔图像的智能识别。实验结果表明,视觉系统能满足长孔道的近距离高分辨全景成像,且实现岩石材质的智能状态评估,在测试集上的识别成功率达到91.49%,基本具备了岩土钻孔状态的综合智能化评估能力。

    Abstract:

    Geotechnical borehole monitoring, as one of the most common tunneling advanced detection techniques, can truly reflect the material properties, characteristics, and groundwater conditions of geomaterials, which is vital to ensure construction safety. Based on the characteristics of the geotechnical borehole monitoring objectives, a smart visual system based on panoramic cameras is developed. The system is suitable for close-range and dynamic high-resolution imaging of the inner walls of long geotechnical boreholes. Based on the improved EfficientNetV2 network and the sliding window prediction, the rapid intelligent recognition of eight types of rock borehole images is realized. Experimental results show that the visual system can meet the requirements for close-range high-resolution panoramic imaging of long boreholes and achieve intelligent state assessment of rock materials. The recognition success rate reaches 91.49% on the test set, and the system preliminarily possesses the comprehensive intelligent evaluation capability of geotechnical borehole status.

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先永利,陈学健,彭真明,汪杰,彭波.基于深度学习的岩石钻孔全景图像识别[J].数据采集与处理,2025,40(3):675-685

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  • 收稿日期:2024-06-23
  • 最后修改日期:2024-09-25
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  • 在线发布日期: 2025-06-13