有限监督下基于视觉的多尺度建筑物体检测

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Yapeng Guo, Yang Xu, Hongtao Cui, Shunlong Li
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引用次数: 0

摘要

当代的多尺度建筑物体检测算法主要依赖于全监督深度学习,需要艰苦耗时的标记过程。本文提出了一种新颖的半监督多尺度建筑物体检测(SS-MCOD),利用近乎无限的无标签图像和有限的标签,实现更准确、更稳健的检测结果。SS-MCOD 采用基于可变形卷积网络(DCN)的师生联合学习框架。DCN 利用可变形优势提取和融合多尺度建筑物体特征。教师模块为未标记图像中的建筑物体生成伪标签,而学生模块则通过伪标签学习标记图像和未标记图像中建筑物体的位置和分类。使用常用建筑数据集进行的实验验证证明了 SS-MCOD 的准确性和泛化性能。这项研究可为建筑领域其他标签有限的检测任务提供启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Vision-Based Multiscale Construction Object Detection under Limited Supervision

Vision-Based Multiscale Construction Object Detection under Limited Supervision

Contemporary multiscale construction object detection algorithms rely predominantly on fully-supervised deep learning, requiring arduous and time-consuming labeling process. This paper presents a novel semisupervised multiscale construction objects detection (SS-MCOD) by harnessing nearly infinite unlabeled images along with limited labels, achieving more accurate and robust detection results. SS-MCOD uses a deformable convolutional network (DCN)-based teacher-student joint learning framework. DCN uses deformable advantages to extract and fuse multiscale construction object features. The teacher module generates pseudolabels for construction objects in unlabeled images, while the student module learns the location and classification of construction objects in both labeled images and unlabeled images with pseudolabels. Experimental validation using commonly used construction datasets demonstrates the accuracy and generalization performance of SS-MCOD. This research can provide insights for other detection tasks with limited labels in the construction domain.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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