Small Target Research Articles

Enhanced Yolov8 network with Extended Kalman Filter for wildlife detection and tracking in complex environments

Amid a growing global focus on ecological conservation and biodiversity monitoring, the efficient identification and tracking of wildlife are essential for environmental research, wildlife protection, and habitat management. Nevertheless, intricate landscapes, varied animal sizes, and obstructions obstruct wildlife detection and tracking. This study introduces the wilDT-YOLOv8n model, specifically engineered for the effective identification and tracking of animals. Initially, the Stable Diffusion model augments the dataset, establishing a basis for training data. Subsequently, enhancements to the Yolov8n model are implemented through the incorporation of the deformable convolutional network DCNv3 and the utilization of the C2f_DCNV3 layer to augment feature extraction efficacy, while addressing detection challenges associated with small targets and intricate backgrounds by integrating the EMGA attention mechanism and the ASPFC feature fusion module. Enhancing the Extended Kalman Filter algorithm guarantees reliable and precise tracking. The research findings reveal that the wilDT-YOLOv8n model attained an average detection accuracy (mAP50) of 88.54 % on the custom dataset, reflecting a 4.57 % enhancement over the original YOLOv8n model; the refined Extended Kalman Filter realizes a Multi-Object Tracking Accuracy (MOTA) of 40.35 %, representing a 3.923 % advancement over the original Kalman Filter. The results indicate the feasibility of accurately detecting and monitoring wildlife in intricate environments, offering significant insights for ecological research and biodiversity conservation, and aiding in the protection of endangered species.

Developmental validation of a multiplex qPCR assay for simultaneous quantification of nuclear and mitochondrial DNA

Quantification of human DNA is key in forensic genetics. A more accurate estimate of the amount of DNA is essential for planning and optimising genotyping assays, as is evaluating the presence of PCR inhibitory substances and DNA degradation status. Multiplex qPCR assays are helpful in forensics because they can quantify different targets simultaneously, thus saving valuable samples, time, and labour. The aim of this study was to highlight the challenges in the developmental validation of a multiplex real-time PCR assay and the drawbacks encountered in translating a previously described and validated assay (SD quants) to a different technology by modifying the dye probes and reagent mix to be used in a different instrument. We developed a TaqMan probe-based multiplex qPCR using reagents and fluorescent probes adapted for the Rotor-Gene 6000 instrument (QIAGEN, Hilden, Germany). The initial assay combined two mitochondrial DNA (mtDNA) and two nuclear DNA (nDNA) targets, with amplification products of different sizes (mtDNA = 69 and 143bp; nDNA = 71 and 181bp), to estimate the DNA degradation status and an internal positive control (IPC) to detect potential inhibitors. During the initial testing of the assay, we observed an interaction between the 69bp mtDNA target and the 71bp nDNA target probe, and experiments were conducted to resolve this issue without success. We removed the small nDNA target (71bp) and changed from a 5-plex to a 4-plex qPCR assay (qMIND). The final tetraplex assay was tested on 105 forensic samples and/or small amounts of degraded DNA, such as bones, teeth, fingernails, formalin-fixed paraffin-embedded tissues (FFPE), and hair shaft samples. The quantification results were compared with data acquired from the same samples using another commercially available quantification system commonly used in forensic laboratories. In addition, the short tandem repeat (STR) profiles were investigated to determine their correlation with the quantitative values obtained. Overall, the qPCR assay was robust and reliable for DNA quantification in samples commonly used in forensic practice.

434P SAT-3247: an oral small molecule inhibitor targeting AAK1, a critical effector of skeletal muscle regeneration

434P SAT-3247: an oral small molecule inhibitor targeting AAK1, a critical effector of skeletal muscle regeneration

Few-shot agricultural pest recognition based on multimodal masked autoencoder

Visual recognition methods based on deep convolutional neural networks have performed well in pest diagnosis and have gradually become a research hotspot. However, agricultural pest recognition faces challenges such as few-shot learning, category imbalance, similarity in appearance, and small pest targets. Existing deep learning-based pest recognition methods typically rely solely on unimodal image data, which results in a model whose recognition performance is heavily dependent on the size and quality of the annotated training dataset. However, the construction of large-scale, high-quality pest datasets requires significant economic and technical costs, limiting the practical generalization of existing methods for pest recognition. To address these challenges, this paper proposes a few-shot pest recognition model called MMAE (multimodal masked autoencoder). Firstly, the masked autoencoder of MMAE integrates self-supervised learning, which can be applied to few-shot datasets and improves recognition accuracy. Secondly, MMAE embeds textual modal information on top of image modal information, thus improving the performance of pest recognition by utilizing the correlation and complementarity between the two modalities. The experimental results show that MMAE is the most effective for pest identification compared with the existing excellent models, and the identification accuracy is as high as 98.12%, which is 1.61 percentage points higher than the current state-of-the-art MAE method. The work in this paper shows that the introduction of textual information can assist the visual coder in capturing agricultural pest characterization information at a higher level of granularity, providing a methodological reference for solving the problem of agricultural pest recognition under few-shot conditions.

An Autonomous Feature Detection Method for Slow-Moving Small Target on Sea Surface Based on Kernelized Contextual Bandit

An Autonomous Feature Detection Method for Slow-Moving Small Target on Sea Surface Based on Kernelized Contextual Bandit

Dual-channel feature extraction hybrid attention network for detecting infrared small targets

Abstract For military early warning, forest fire prevention, and maritime search and rescue, infrared small target detection is critical. However, because of the low contrast and inconspicuous features of infrared small targets, rendering most existing methods ineffective in restoring target edge details or misidentifying the background as a target. This paper proposes a dual-channel feature extraction network (DCFE-Net) with hybrid attention, which enables the network to suppress the background and enhance the target by designing dual-channel feature extraction and multi-layer feature fusion. Specifically, the dual-channel mainly consists of a convolutional attention fusion module, which adaptively integrates feature map correlations by introducing a hybrid attention module to capture global information while enhancing the feature representation of small targets, and a feature compression extraction module, which utilizes depth-separable convolutional combinations to carry out fine-grained target feature extraction while reducing the loss of details. In addition, the multilevel feature enhancement module ensures that the network can capture targets at different scales through skip connection operations, while avoiding small targets from being overwhelmed by deep features, making them simultaneously semantically informative and detailed. Therefore, the network can fuse multilevel features for effective information extraction. According to the experimental results, DCFE-Net performs best in false alarm rate and detection probability.

Research on traffic sign detection model with improved YOLOv8s

Abstract With the rapid advancement of intelligent driving technologies such as autonomous driving and driver assistance, the significance of traffic sign recognition in road and traffic safety has become increasingly prominent. Addressing the challenges posed by the current mainstream traffic sign detection models, which include complex background structures and the detection of small targets, this study proposes an enhanced network for detecting traffic signs based on YOLOv8s. Firstly, we modify the convolutional component of the backbone by replacing the initial convolutions with the Alterable Kernel Convolution (AKConv) network, thereby enhancing feature extraction efficiency and network performance. Secondly, we introduce the Global Attention Mechanism (GAM) within the network to effectively aggregate features, improve reconstruction performance, and maintain low computational and storage costs. Lastly, we integrate the Bi-directional Feature Pyramid Network (BiFPN) into the YOLOv8s framework, enabling the model to capture multi-scale features efficiently and enhance recognition accuracy. Experimental results conducted on the CCTSDB-2021 show the efficacy of the study improved YOLOv8s model. Precision, recall, and mAP@50% achieve impressive values of 96.0%, 79.4%, and 88.3%, respectively. These metrics represent a 4.6% improvement in precision, a 0.4% enhancement in the recall, and a 2% enhancement in mAP@50%, compared with the original YOLOv8s model. Moreover, this improved model exhibits greater precision when compared with other existing models.

Detection of walnut internal quality via improved YOLOv5 and x‐ray imaging

AbstractThis study proposes a walnut target detection algorithm based on improved YOLOv5 and x‐ray imaging to meet the demand for internal quality detection and removal in the Xinjiang walnut industry. By replacing the C3 module in the backbone layer with the C2f module and the couple‐head in the head layer with the decouple‐head, the algorithm reduces computational complexity, enhances robustness and generalizability, and retains more spatial information, thereby improving the performance of multicategory small target detection. In addition, this paper replaces the original CIOU loss function with the EIOU loss function to improve the convergence speed of the algorithm's accuracy and boundary aspect ratio. Compared with the original model, the improved model, improved YOLOv5, maintains the same average precision for normal walnuts while increasing the average precision for shriveled walnuts and empty‐shell walnuts by 8.2% and 0.4%, respectively. Compared with other mainstream models, such as VGG16, ResNet50, YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s, this model achieves the highest detection accuracy and good detection performance, with a single‐image detection time of 11.9 ms, meeting the requirements for real‐time detection. This work lays a foundation for automatic robot detection of the internal quality and removal of walnuts, showing practical application potential.Practical applicationsXinjiang stands as a prominent producer of walnuts; however, due to the decentralized nature of its cultivation and management processes, a notable prevalence of internal empty shells and shrinkage is observed, which substantially diminishes their commercial value. The integration of YOLOv5 with x‐ray imaging technology promises to enhance the precision of internal quality assessments of walnuts. The improved YOLOv5 model, as delineated in this study, exhibits the highest detection accuracy when benchmarked against a multitude of other models. It achieves a detection latency of merely 11.9 ms per image, thereby satisfying the stringent demands for real‐time detection applications. This model is designed for integration into x‐ray systems with an adjunctive sorting mechanism, which facilitates the inspection and exclusion of defective walnuts on conveyor belts.

Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Hardware-Accelerated Infrared Small Target Recognition Based on Energy-Weighted Local Uncertainty Measure

Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively.

A Ghost and Attention Mechanism-Based Deep Learning Approach for SAR Small Target Image Detection

Sy nthetic aperture radar (SAR) images play an important role in the task of small target imaging and detection. However, due to its blurred target imaging, complex image background, and challenging feature representation, SAR detection is difficult to get high precision in a complex imaging environment. Therefore, by integrating the ghost model and the Attention-Mechanism (AM), this paper presents a new feature network model based on the improved YOLOv5s for detecting small targets in SAR images efficiently. The key is to design a Ghost_AM module by integrating the ghost bottleneck with the simple AM. This Ghost_AM module is introduced into to the YOLOv5s’ network for improving the YOLOv5s’ extraction performance for small target’s feature from a complex SAR image. Furthermore, to improve the YOLOv5s’ recognition ability for the features extracted from the Ghost_AM, a bidirectional feature pyramid network is use to replace the YOLOv5s’ feature fusion network and get a precise detection. The experiments using some SAR ship image datasets have demonstrated the presented model is better than some traditional SAR target detection algorithms and the original YOLOv5s. Moreover, the presented model’s parameters are reduced by 25.1% compared with the original YOLOv5s, which contributes to its simpler implementation.

Underwater Target Detection Using Side-Scan Sonar Images Based on Upsampling and Downsampling

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature maps, which may cause information loss for small targets and decrease performance in SSS images. To address this problem, based on the yolov8 network, we proposed a new underwater target detection model based on upsampling and downsampling. Firstly, we introduced a new general downsampling module called shallow robust feature downsampling (SRFD) and a receptive field convolution (RFCAConv) in the backbone network. Thereby multiple feature maps extracted by different downsampling techniques can be fused to create a more robust feature map with a complementary set of features. Additionally, an ultra-lightweight and efficient dynamic upsampling module (Dysample) is introduced to improve the accuracy of the feature pyramid network (FPN) in fusing different levels of features. On the underwater shipwreck dataset, our improved model’s mAP50 increased by 4.4% compared to the baseline model.

Arc bubble edge detection method based on deep transfer learning in underwater wet welding.

The stability of arc bubble is a crucial indicator of underwater wet welding process. However, limited research exists on detecting arc bubble edges in such environments, and traditional algorithms often produce blurry and discontinuous results. To address these challenges, we propose a novel arc bubble edge detection method based on deep transfer learning for processing underwater wet welding images. The proposed method integrates two training stages: pre-training and fine-tuning. In the pre-training stage, a large source domain dataset is used to train VGG16 as a feature extractor. In the fine-tuning stage, we introduce the Attention-Scale-Semantics (ASS) model, which consists of a Convolutional Block Attention Module (CBAM), a Scale Fusion Module (SCM) and a Semantic Fusion Module (SEM). The ASS model is further trained on a small target domain dataset specific to underwater wet welding to fine-tune the model parameters. The CBAM can adaptively weight the feature maps, focusing on more crucial features to better capture edge information. The SCM training method maximizes feature utilization and simplifies training by combining multi-scale features. Additionally, the skip structure of SEM effectively mitigates semantic loss in the high-level network, enhancing the accuracy of edge detection. On the BSDS500 dataset and a self-constructed underwater wet welding dataset, the ASS model was evaluated against conventional edge detection models-Richer Convolutional Features (RCF), Fully Convolutional Network (FCN), and UNet-as well as state-of-the-art models LDC and TEED. In terms of Mean Absolute Error (MAE), accuracy, and other evaluation metrics, the ASS model consistently outperforms these models, demonstrating edge detection capabilities that are both effective and stable in detecting arc bubble edges in underwater wet welding images.

A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial–spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing the detection and background suppression capabilities. The performance of the proposed approach is evaluated using the average scores of AUCD,F, AUCF,τ, AUCBS, and AUCSNPR. The corresponding values are 0.99660, 0.00078, 0.99587, and 333.629, respectively. These results demonstrate the accuracy of the model in different evaluation metrics, with AUCD,F achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

N6-Methyladenosine-mediated phase separation suppresses NOTCH1 expression and promotes mitochondrial fission in diabetic cardiac fibrosis.

N6-methyladenosine (m6A) modification of messenger RNA (mRNA) is crucial for liquid-liquid phase separation in mammals. Increasing evidence indicates that liquid-liquid phase separation in proteins and RNAs affects diabetic cardiomyopathy. However, the molecular mechanism by which m6A-mediated phase separation regulates diabetic cardiac fibrosis remains elusive. Leptin receptor-deficient mice (db/db), cardiac fibroblast-specific Notch1 conditional knockout (POSTN-Cre × Notch1flox/flox) mice, and Cre mice were used to induce diabetic cardiac fibrosis. Adeno-associated virus 9 carrying cardiac fibroblast-specific periostin (Postn) promoter-driven small hairpin RNA targeting Alkbh5, Ythdf2, or Notch1, and the phase separation inhibitor 1,6-hexanediol were administered to investigate their roles in diabetic cardiac fibrosis. Histological and biochemical analyses were performed to determine how Alkbh5 and Ythdf2 regulate Notch1 expression in diabetic cardiac fibrosis. NOTCH1 was reconstituted in ALKBH5- and YTHDF2-deficient cardiac fibroblasts and mouse hearts to study its effects on mitochondrial fission and diabetic cardiac fibrosis. Heart tissue samples from patients with diabetic cardiomyopathy were used to validate our findings. In mice with diabetic cardiac fibrosis, decreased Notch1 expression was accompanied by high m6A mRNA levels and mitochondrial fission. Fibroblast-specific deletion of Notch1 enhanced mitochondrial fission and cardiac fibroblast proliferation and induced diabetic cardiac fibrosis in mice. Notch1 downregulation was associated with Alkbh5-mediated m6A demethylation in the 3'UTR of Notch1 mRNA and elevated m6A mRNA levels. These elevated m6A levels in Notch1 mRNA markedly enhanced YTHDF2 phase separation, increased the recognition of m6A residues in Notch1 mRNA by YTHDF2, and induced Notch1 degradation. Conversely, epitranscriptomic downregulation rescues Notch1 expression, resulting in the opposite effects. Human heart tissues from patients with diabetic cardiomyopathy were used to validate the findings in mice with diabetic cardiac fibrosis. We identified a novel epitranscriptomic mechanism by which m6A-mediated phase separation suppresses Notch1 expression, thereby promoting mitochondrial fission in diabetic cardiac fibrosis. Our findings provide new insights for the development of novel treatment approaches for patients with diabetic cardiac fibrosis.

Spatio-temporal fusion with motion masks for the moving small target detection from remote-sensing videos

Moving small target detection is an eye-catching research topic, full of great challenges in object detection field. Its primary difficulties exist in (i) the very small size proportion of the target region in background images, (ii) the quite weak contrast to background and (iii) the perception of slight target motion. Currently-existing detection methods mainly utilize the image features from spatial domain. This kind of features is quite weak to small targets. More new feature kinds need to be concerned. In view of these, besides traditional image clues, motion features are currently attracting more and more attention in small target detection. In order to promote detection performance, this paper proposes a Spatio-Temporal Fusion Network (STFNet) with two parallel feature extraction branches for detecting moving small targets. In this network, one branch is designed to capture the traditional semantic features from spatial domain, and the other is devised to perceive the slight target motion features hidden in temporal domain. Meanwhile, to optimize the extraction of spatio-temporal features, a group of motion masks is specially designed to guide feature extractors to pay more precise attention to the positions where small targets appear. Moreover, we design a new bridging module to enhance the cross-domain and cross-scale fusion of spatio-temporal features. Through extensive comparison and ablation experiments on seven sub-datasets, it is proved that our new STFNet is effective and it is obviously superior to the compared ones in detecting moving small targets from satellite sequence images. It achieves a Precision of 0.93, an mAP50 of 71.10% and an F1 of 0.84, evidently higher than existing state-of-the-art methods. Our codes are available at https://github.com/UESTC-nnLab/STF.

A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients

PurposeRecent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of prognostic factors.Materials and methodsA dataset of 478 NSCLC patients treated with SBRT (IMRT or VMAT) was analyzed. We developed a deep learning model for DM prediction and explainable AI was used to identify the most significant prognostic factors. Subsequently, the prognostic power of the extracted features and clinical details were analyzed using conventional statistical methods.ResultsTreatment technique, tumor features, and dosiomic features in a 3 cm wide ring around the PTV (PTV3cm) were identified as the strongest predictors of DM. The Hazard Ratio (HR) for Dmean,PTV3cm was significantly above 1 (p < 0.001). There was no significance of the PTV3cm dose after treatment technique stratification. However, the dose in PTV3cm was found to be a highly significant DM predictor (HR > 1, p = 0.004) when analyzing only VMAT patients with small and spherical tumors (i.e., sphericity > 0.5).ConclusionsThe main reason for conflicting conclusions in previous papers was inconsistent datasets and insufficient consideration of confounding variables. No causal correlation between the risk of DM and dose outside the PTV was found. However, the mean dose to PTV3cm can be a significant predictor of DM in small spherical targets treated with VMAT, which might clinically imply considering larger PTV margins for smaller, more spherical tumors (e.g., if IGTV > 2 cm, then margin ≤ 7 mm, else margin > 7 mm).

Research on a Metal Surface Defect Detection Algorithm Based on DSL-YOLO

In industrial manufacturing, metal surface defect detection often suffers from low detection accuracy, high leakage rates, and false detection rates. To address these issues, this paper proposes a novel model named DSL-YOLO for metal surface defect detection. First, we introduce the C2f_DWRB structure by integrating the DWRB module with C2f, enhancing the model’s ability to detect small and occluded targets and effectively extract sparse spatial features. Second, we design the SADown module to improve feature extraction in challenging tasks involving blurred images or very small objects. Finally, to further enhance the model’s capacity to extract multi-scale features and capture critical image information (such as edges, textures, and shapes) without significantly increasing memory usage and computational cost, we propose the LASPPF structure. Experimental results demonstrate that the improved model achieves significant performance gains on both the GC10-DET and NEU-DET datasets, with a mAP@0.5 increase of 4.2% and 2.6%, respectively. The improvements in detection accuracy highlight the model’s ability to address common challenges while maintaining efficiency and feasibility in metal surface defect detection, providing a valuable solution for industrial applications.

Unveiling the power of Haar frequency domain: Advancing small target motion detection in dim light

Visual small target motion detection finds successful applications in varied scenarios. However, dim-light conditions, such as the tunnel scenes and nighttime environments, present significant challenges to existing detection methods which mainly operate within the spatiotemporal domain. This is because the transmission of small target motion information suffers from the inevitable interference of image noise caused by dim light in the spatiotemporal domain, resulting in the detriment of extracting essential spatiotemporal features of the target motion. Given the significant obstacles posed by dim-light imaging to small target motion detection within the spatiotemporal domain, the exploration of an alternative observation domain for small target motion, alongside the development of a corresponding detection method, emerges as a viable solution. To address this, in this paper, we discovered the remarkable potential of the Haar frequency domain in characterizing the small target motion in dim light. To investigate the advantages of integrating Haar frequency processing in small target motion detection, we introduce a Haar-windowed summation mechanism into an existing bio-inspired small target motion detection model. The proposed mechanism integrates visual information in spatiotemporal windows regulated by frequency parameters of Haar wavelets and effectively discriminates the small target motion from the disturbance of random noise caused by dim light. Theoretical analysis and numerical experiments confirm the superior performance of integrating the Haar frequency processing. This study provides a new vision of small target motion detection through the lens of the frequency domain and extends the limits of existing bio-inspired models for practical applications in dim light.

Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings

In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. To solve the problem of insufficient feature extraction of small targets in bridge disease images under complex backgrounds and noise, we propose the YOLOv8 Dynamic Plus model. First, we enhanced attention on multi-scale disease features by implementing structural reparameterization with parallel small-kernel expansion convolution. Next, we reconstructed the relationship between localization and classification tasks in the detection head and implemented dynamic selection of interactive features using a feature extractor to improve the accuracy of classification and recognition. Finally, to address problems of missed detection, such as inadequate extraction of small targets, we extended the original YOLOv8 architecture by adding a layer in the feature extraction phase dedicated to small-target detection. This modification integrated the neck part more effectively with the shallow features of the original three-layer YOLOv8 feature extraction stage. The improved YOLOv8 Dynamic Plus model demonstrated a 7.4 percentage-point increase in performance compared to the original model, validating the feasibility of our approach and enhancing its capability for building disease detection. In practice, this improvement has led to more accurate maintenance and safety assessments of concrete buildings and earlier detection of potential structural problems, resulting in lower maintenance costs and longer building life. This not only improves the safety of buildings but also brings significant economic benefits and social value to the industries involved.