Unsupervised Vision-Based Structural Anomaly Detection and Localization with Reverse Knowledge Distillation

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Xiaoming Lei, Mengjin Sun, Rongxin Zhao, Huayong Wu, Zijie Zhou, You Dong, Limin Sun
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引用次数: 0

Abstract

Most of vision-based methods for structural damage detection rely on supervised learning, requiring a substantial number of labeled images for model training, which is labor-intensive and time-consuming. To address these challenges, this study introduces a vision-based structural anomaly detection and localization approach using unsupervised learning and reverse knowledge distillation. The proposed model incorporates a teacher model, a student model, and a trainable one-class bottleneck embedding module. The asymmetrical architecture of the teacher and student models forms an encoder-decoder structure for parameter transfer and feature extraction. The student network receives a specific embedding from the teacher network as input and target, facilitating the recovery of multiscale information from the teacher. Training images only contain the undamaged structures, and the teacher model, a pretrained model, instructs the student model to remember their undamaged features to detect and localize damages in unseen testing images. Through experiments, including a comparison among five candidate backbones for pretrained teacher models based on the residual network and testing across various structural damage types, the optimal model is identified, demonstrating good performance in both anomaly detection and localization. Furthermore, the model’s generalization performance is thoroughly validated, confirming its efficacy across diverse scenarios.

Abstract Image

基于视觉的无监督结构异常检测和定位与反向知识提炼
大多数基于视觉的结构损伤检测方法都依赖于监督学习,需要大量的标注图像进行模型训练,耗费大量人力和时间。为了应对这些挑战,本研究采用无监督学习和反向知识提炼的方法,介绍了一种基于视觉的结构异常检测和定位方法。所提出的模型包含一个教师模型、一个学生模型和一个可训练的一类瓶颈嵌入模块。教师模型和学生模型的非对称结构形成了一个用于参数传输和特征提取的编码器-解码器结构。学生网络接收教师网络的特定嵌入作为输入和目标,便于从教师网络中恢复多尺度信息。训练图像只包含未损坏的结构,教师模型作为一个预训练模型,指示学生模型记住未损坏的特征,以检测和定位未见测试图像中的损坏。通过实验,包括对基于残差网络的预训练教师模型的五个候选骨架进行比较,以及对各种结构损伤类型进行测试,最终确定了最佳模型,在异常检测和定位方面都表现出了良好的性能。此外,该模型的泛化性能也得到了全面验证,证实了其在不同场景下的有效性。
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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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