Thermo-LIO: A Novel Multi-Sensor Integrated System for Structural Health Monitoring
- URL: http://arxiv.org/abs/2601.08977v1
- Date: Tue, 13 Jan 2026 20:54:10 GMT
- Title: Thermo-LIO: A Novel Multi-Sensor Integrated System for Structural Health Monitoring
- Authors: Chao Yang, Haoyuan Zheng, Yue Ma,
- Abstract summary: Traditional two-dimensional thermography is limited in effectively assessing complex geometries, inaccessible areas, and subsurface defects.<n>This paper introduces Thermo-LIO, a novel multi-sensor system that can enhance Structural Health Monitoring (SHM) by fusing thermal imaging with high-resolution LiDAR.
- Score: 7.407155043542133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traditional two-dimensional thermography, despite being non-invasive and useful for defect detection in the construction field, is limited in effectively assessing complex geometries, inaccessible areas, and subsurface defects. This paper introduces Thermo-LIO, a novel multi-sensor system that can enhance Structural Health Monitoring (SHM) by fusing thermal imaging with high-resolution LiDAR. To achieve this, the study first develops a multimodal fusion method combining thermal imaging and LiDAR, enabling precise calibration and synchronization of multimodal data streams to create accurate representations of temperature distributions in buildings. Second, it integrates this fusion approach with LiDAR-Inertial Odometry (LIO), enabling full coverage of large-scale structures and allowing for detailed monitoring of temperature variations and defect detection across inspection cycles. Experimental validations, including case studies on a bridge and a hall building, demonstrate that Thermo-LIO can detect detailed thermal anomalies and structural defects more accurately than traditional methods. The system enhances diagnostic precision, enables real-time processing, and expands inspection coverage, highlighting the crucial role of multimodal sensor integration in advancing SHM methodologies for large-scale civil infrastructure.
Related papers
- Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks [1.0499611180329804]
We present a data-driven framework that infers temperature fields from geometric phase in a canonical pool boiling configuration.<n>Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport.
arXiv Detail & Related papers (2025-05-01T19:26:01Z) - Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
arXiv Detail & Related papers (2024-11-27T12:18:39Z) - Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms.
We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds.
Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
arXiv Detail & Related papers (2024-07-26T10:29:16Z) - In-Situ Infrared Camera Monitoring for Defect and Anomaly Detection in Laser Powder Bed Fusion: Calibration, Data Mapping, and Feature Extraction [0.26999000177990923]
Laser powder bed fusion (LPBF) process can incur defects due to melt pool instabilities, spattering, temperature increase, and powder spread anomalies.
Identifying defects through in-situ monitoring typically requires collecting, storing, and analyzing large amounts of data generated.
arXiv Detail & Related papers (2024-07-17T16:02:22Z) - Lightweight Multi-System Multivariate Interconnection and Divergence Discovery [0.0]
This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in multi-system environments.
Our experiment on the readout systems of the Hadron Calorimeter of the Compact Muon Solenoid (CMS) experiment at CERN demonstrates the effectiveness of the proposed method.
arXiv Detail & Related papers (2024-04-12T13:02:33Z) - Critical heat flux diagnosis using conditional generative adversarial
networks [0.0]
The critical heat flux (CHF) is an essential safety boundary in boiling heat transfer processes employed in high heat flux thermal-hydraulic systems.
This study presents a data-driven, image-to-image translation method for reconstructing thermal data of a boiling system at CHF.
arXiv Detail & Related papers (2023-05-04T07:53:04Z) - Target-aware Dual Adversarial Learning and a Multi-scenario
Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
This study addresses the issue of fusing infrared and visible images that appear differently for object detection.
Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks.
This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
arXiv Detail & Related papers (2022-03-30T11:44:56Z) - Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
CT Reconstruction [108.06731611196291]
We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies.
We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
arXiv Detail & Related papers (2022-03-10T14:22:54Z) - Tracking perovskite crystallization via deep learning-based feature
detection on 2D X-ray scattering data [137.47124933818066]
We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture.
We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
arXiv Detail & Related papers (2022-02-22T15:39:00Z) - Disentangling multiple scattering with deep learning: application to
strain mapping from electron diffraction patterns [48.53244254413104]
We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images.
We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures.
Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
arXiv Detail & Related papers (2022-02-01T03:53:39Z) - Thermal vulnerability detection in integrated electronic and photonic
circuits using IR thermography [1.8809094132625916]
This work presents an IR-assisted thermal vulnerability detection technique suitable for photonic as well as electronic components.
For the first time, the reliability testing is extended to a fully functional microwave photonic system using conventional IR thermography.
arXiv Detail & Related papers (2020-05-01T09:25:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.