Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard, Leanne; Debono, Carl James; Valentino, Gianluca; Castro, Mario Di

doi:10.3390/s21124040

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…The DLF models fuse multichannel/multiscale information and typically produce more consistent and better prediction performance than individual models, have good noise immunity, can handle high-dimensional data, provide complete and detailed object information, and are simple to implement and fast to train [32,33]. These models are extensively used in the fields of injury detection, artificial intelligence, and image processing [34][35][36]. Based on previous studies, machine learning and hyperspectral imagery have been used successfully in many applications, but the strategy based on DLF model fusion has not yet been applied to crop yield prediction [37,38].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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Section: Introductionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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“…However, the dark environment of the tunnel and the presence of pipes, cables, and stains can influence image quality and detection accuracy. Attard et al [242] introduced a shading algorithm to correct the images for light and segmented the reflective areas of pipes for shielding using U-net, thus reducing recognition errors.…”

Section: Other Buildings and Infrastructurementioning

confidence: 99%

“…Improving image quality can avoid false negatives and positives as much as possible. Attard et al [242] fully considered the influence of uneven illumination, stains, pipes, cables, and other factors in the tunnel environment and then adopted image fusion and DL methods to realize the damage detection of tunnel lining cracks.…”

Section: Data Issuesmentioning

confidence: 99%

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Jia,

2023

Sensors

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Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility’s characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM.

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“…In [39,47,63], only a comparison between algorithms was performed. In [40,45,52,[56][57][58]62,69,72,[77][78][79][80]85] a comparison with damage, deformation, and, in general, real-world manual measurements was performed. Finally, a proof of concept was provided in [84], with only qualitative results.…”

Section: Validation With Gold-standardmentioning

confidence: 99%

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Ferraris

Amprimo

Pettiti

2023

Signals

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Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

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Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Cited by 11 publications

References 35 publications

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

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