基于CutMix数据增强与多约束损失函数的YOLOv7盾构隧道渗漏水检测

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万方数据
维普

中文摘要：盾构隧道强度投影图像中渗漏水尺寸不一致且像素占比偏小,现有目标检测模型的关键特征学习能力较弱,存在渗漏水病害目标检测精度偏低的问题.本文提出了基于CutMix数据增强与多约束损失函数的改进YOLOv7盾构隧道渗漏水检测方法.首先采用镶嵌CutMix方法对隧道图像进行数据增强,将多张不同的训练样本进行随机裁剪,拼接融合成具有综合特征的新样本;然后以YOLOv7网络为骨架结构,引入高效通道注意力模块,提高渗漏水关键特征的自主学习与表达能力;最后引入多约束几何条件的损失函数,提高预测框几何形状的精度,从而提升模型预测精度.在光线良好、光线不佳和存在遮挡等复杂环境情况下,选取Fast R-CNN、SSD、YOLOv5、YOLOv7这4种算法进行对比,试验表明,本文算法渗漏水检测精度达85.90％,平均精度比同类算法分别提高5.55％、8.89％、3.93％、2.75％,具有较高的稳健性和泛化能力.

外文标题：YOLOv7 shield tunnel water leakage detection method based on CutMix data augmentation and multi-constraint loss function

外文摘要：Since the size of leakage water in the intensity projection image of the shield tunnel is inconsistent and the proportion of pixels is limited,the learning ability of key features of object detection models is weak.As a result,the detection accuracy of leakage disease targets is too low to meet the requirements of the application.Therefore,an improved YOLOv7 shield tunnel leakage water detection method based on CutMix data enhancement and multi-constraint loss function is proposed to address the issue in this paper.Firstly,the tunnel images are enhanced using the embedded CutMix approach.Various training samples are randomly combined into new samples with comprehensive features.Secondly,the YOLOv7 network is employed as the skeletal structure,and an efficient channel attention module is introduced to enhance the ability of crucial leakage features to learn and express themselves autonomously.Finally,a loss function incorporating multi-constraint geometric conditions is designed to improve the accuracy of the geometric shape of the prediction box,thereby improving the model's predictive accuracy.The four algorithms included Fast R-CNN,SSD,YOLOv5,and YOLOv7 are chosen for comparison in complex environments with good lighting,poor lighting,and occlusion.The experiments show that our algorithm achieves a leakage detection accuracy of 85.90％.The average accuracy of the proposed method is higher than Fast R-CNN,SSD,YOLOv5,and YOLOv7 by 5.55％,8.89％,3.93％,and 2.75％,respectively.It exhibits good robustness and generalization ability.

外文关键词：

leakage water detectionefficient channel attentionmulti-constraint geometric conditionsshield tunnelingYOLOv7

作者：

高贤君、刘振宇、许磊、黄仡凡、谭美淋、熊文豪、杨元维

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作者单位：

长江大学地球科学学院,湖北武汉 430100

中国铁路设计集团有限公司,天津 300251

福建省海丝数字科技有限公司,福建福州 350000

内蒙古自治区测绘地理信息中心,内蒙古呼和浩特 010050

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关键词：

渗漏水检测 ECA 多约束几何条件盾构隧道 YOLOv7

基金：

城市轨道交通数字化建设与测评技术国家工程实验室开放课题基金湖南科技大学测绘遥感信息工程湖南省重点实验室开放基金自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金天津市科技计划天津市科技计划湖南省自然科学基金项目部门联合基金

项目编号：

2023ZH01E22205MEMI-2021-2022-0823YFYSHZ0019023YFZCSN002802024JJ8327

出版年：

2024

DOI：

10.13474/j.cnki.11-2246.2024.0719

测绘通报

测绘出版社

测绘通报

CSTPCD北大核心

影响因子：1.027

ISSN：0494-0911

年,卷(期)：2024.(7)

参考文献量3