Application Of Convolutional Neural Networks Research Articles

Improving Agricultural Safety Through Deep Neural Networks for Intrusion Monitoring

The intersection of agricultural activities and natural habitats presents several implications, one of the most pressing being the intrusion of various species into crop fields. To address the continued and growing challenge of discovering methods for successful mitigation and protection of crops, a new method must be introduced. Current practices, such as using pheromones and monitoring by humans, yield suboptimal results due to inefficiency, labour-intensive processes, and environmental damage. This paper proposes a novel method based on deep learning technologies, namely UNet and EfficientNet-B7 architectures, for semantic segmentation of crop-damaging species in agricultural habitats. The method provides a reliable means to detect species intrusion by integrating it into automated monitoring operations. Whether identifying the segmentation point of target species or utilizing feature-based detection with the proposed model, the application of advanced convolutional neural networks ensures the accuracy of these systems. This method demonstrates higher performance in species identification and proposes newer and ecologically friendly methods of implementing non-invasive monitoring for conservation alongside agriculture. The experimental results confirm that the proposed UNet with EfficientNet-B7 achieves a high accuracy ratio of 95.3%, an F1-score ratio of 92.4%, and an efficiency ratio of 97.6% compared to other existing models.

Integration of Convolutional Neural Networks and Human-Computer Interaction for Advancing Art Design

This paper explores the innovative application of convolutional neural networks (CNNs) and human-computer interaction (HCI) in art design, facilitated by advanced web-services technology. The integration of these technologies aims to streamline the creative process, making it more accessible and cost-effective for artists. By leveraging deep learning algorithms for style transfer and visual content processing, this research demonstrates how CNNs can transform user inputs into unique artworks. Furthermore, HCI principles ensure a seamless interface between users and digital tools, enhancing usability and efficiency. This study also addresses the challenges associated with artificial intelligence in art creation, including ethical considerations and the preservation of cultural heritage. Through practical implementations and case studies, the paper highlights the potential for web-based platforms to democratize art creation while maintaining artistic integrity.

CNN-SENet: a GNSS-R ocean wind speed retrieval model integrating CNN and SENet attention mechanism

The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry (GNSS-R). The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network (CNN) models to retrieve sea surface wind speed from GNSS-R observables. However, the standard CNN models assign equal weight to all features, overlooking the more relevant ones, which reduces training efficiency and accuracy. To address this issue, this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network (SENet) attention mechanism, named CNN-SENet. The CNN-SENet model increases the weight for important features while suppressing the weight for less relevant ones, thereby improving accuracy and training efficiency. Results indicate that the CNN-SENet demonstrates a significant advantage in training efficiency over the standard CNN, reducing training time by nearly half. Additionally, the CNN-SENet model predicts the wind speeds in the range of 0–40 m/s with a Root Mean Square Error (RMSE) of 1.29 m/s and a coefficient of determination (R2) of 62.4%. It also outperforms both the standard CNN and the Geophysical Model Function (GMF), improving wind speed accuracy by 0.14 m/s and 0.62 m/s, respectively. Furthermore, the CNN-SENet model exhibits superior temporal generalization compared to the standard CNN.

Application of Convolutional Neural Network Denoising to Improve Cone Beam CT Myelographic Images.

Cone beam CT is an imaging modality that provides high-resolution, cross-sectional imaging in the fluoroscopy suite. In neuroradiology, cone beam CT has been used for various applications including temporal bone imaging and during spinal and cerebral angiography. Furthermore, cone beam CT has been shown to improve imaging of spinal CSF leaks during myelography. One drawback of cone beam CT is that images have a relatively high noise level. In this technical report, we describe the first application of a high-resolution convolutional neural network to denoise cone beam CT myelographic images. We show examples of the resulting improvement in image quality for a variety of types of spinal CSF leaks. Further application of this technique is warranted to demonstrate its clinical utility and potential use for other cone beam CT applications.ABBREVIATIONS: CBCT = cone beam CT; CB-CTM = cone beam CT myelography; CTA = CT angiography; CVF = CSF-venous fistula; DSM = digital subtraction myelography; EID = energy integrating detector; FBP = filtered back-projection; SNR = signal-to-noise ratio.

Global-scale explainable AI assessment for OBIA-based classification using Deep Learning and Machine Learning methods

Over the past decade, object-based image analysis (OBIA) has gained prominence as a widely adopted method for generating land use/land cover (LULC) maps. This study aims to evaluate the performance of various classification algorithms within the OBIA framework using SPOT-6 satellite imagery. The research methodology involved segmenting the images with the multi-resolution segmentation (MRS) algorithm, followed by the application of convolutional neural networks (CNN), random forest (RF), and support vector machine (SVM) algorithms for classification. The study was conducted in the Perpignan province, located in the Pyrénées-Orientales region of France. After the segmentation stage, CNN, RF, and SVM classifiers were employed to classify the image segments based on both spectral and spatial attributes. The accuracy of the resulting thematic maps was assessed using standard metrics, including overall accuracy (OA), the Kappa coefficient (KC), and the F��score (FS). Of the three classifiers, CNN achieved the highest overall accuracy at 91.28%, outperforming SVM, which attained an OA of 90.50%, and RF, which recorded an OA of 87.28%. Additionally, this study explored the integration of explainable artificial intelligence (AI) techniques, specifically the Shapley Additive Explanations (SHAP) algorithm, to enhance the interpretability of the machine learning models. This approach fosters greater trust, accountability, and acceptance in decision-making processes. By leveraging SHAP values, the study provides deeper insights into the decision-making processes of the CNN, SVM, and RF classifiers, ultimately enhancing the transparency and comprehensibility of these models.

A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment predictive capabilities. These models undergo systematic validation using historical events and real-world data to assess their reliability, robustness, and practical relevance. Furthermore, the study evaluates the potential of these advanced prediction models to inform disaster risk management and mitigation strategies. By leveraging deep learning techniques and optimizing CNN structures, this research aims to refine forecasting precision, supporting proactive disaster preparedness. The anticipated outcome is an improved predictive framework that enhances early warning systems, facilitates informed decision-making, and strengthens emergency response mechanisms. Ultimately, this study contributes to the broader goal of increasing resilience against natural disasters by equipping policymakers, emergency responders, and urban planners with more accurate and timely risk assessments.

Comprehensive study of various machine learning models for plant disease detection: Analysis of deep models on tomato plant

Agriculture is a crucial component for an overpopulated country like India, and thus, plant diseases present a substantial risk to the output of crops, thereby making timely identification and diagnosis crucial for guaranteeing robust economic development. Tomatoes, being a prominent agricultural commodity, are vulnerable to a diverse range of illnesses, which can be detected by observing foliage signs. Automated identification, categorization, and assessment of the severity of plant diseases utilizing Artificial Intelligence have become crucial for improving agricultural productivity. Advancements in machine learning, notably in the application of convolutional neural networks (CNNs), provide potential solutions for precisely detecting and categorizing tomato plant illnesses. These automated solutions minimize the requirement for manual inspection, which is both very labor-intensive and susceptible to errors. This work investigates machine learning methods for detecting plant diseases and provides an evaluation of advanced deep learning approaches used for detecting and classifying tomato plant diseases. MobileNet, ResNet, and DenseNet models exhibited greater performance among the six models examined. To improve the interpretability of deep learning models, Grad-CAM (Gradient-weighted Class Activation Mapping) is utilized. The performance of this method is evaluated using high-performing models such as MobileNet, ResNet, and DenseNet, which are commonly used for plant disease detection.

Application of Convolutional Neural Networks in Animal Husbandry: A Review

Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their high-performance accuracy in analyzing images and videos. Monitoring animals’ health is important for their welfare, food abundance, safety, and economic productivity. This paper aims to comprehensively review recent advancements and applications of relevant models that are based on CNNs for livestock health monitoring, covering the detection of their various diseases and classification of their behavior, for overall management gain. We selected relevant articles with various experimental results addressing animal detection, localization, tracking, and behavioral monitoring, validating the high-performance accuracy and efficiency of CNNs. Prominent anchor-based object detection models such as R-CNN (series), YOLO (series) and SSD (series), and anchor-free object detection models such as key-point based and anchor-point based are often used, demonstrating great versatility and robustness across various tasks. From the analysis, it is evident that more significant research contributions to animal husbandry have been made by CNNs. Limited labeled data, variation in data, low-quality or noisy images, complex backgrounds, computational demand, species-specific models, high implementation cost, scalability, modeling complex behaviors, and compatibility with current farm management systems are good examples of several notable challenges when applying CNNs in animal husbandry. By continued research efforts, these challenges can be addressed for the actualization of sustainable animal husbandry.

Enhanced Brain Tumor Classification Using MobileNetV2: A Comprehensive Preprocessing and Fine-Tuning Approach

Background: Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. Brain tumors can be broadly classified into two categories: primary tumors, which originate within the brain, and secondary tumors, which are metastatic in nature. Effective glioma, meningioma, and pituitary tumor diagnosis and treatment requires the precise differentiation of these tumors as well as non-tumors for improved clinical outcomes. Methods: Here, we present a new method to classify brain tumors based on the MobileNetV2 architecture with advanced preprocessing for high accuracy. We accessed an MRI image dataset from Kaggle that contained 1311 images in the test set. We split the data into 80% training and 20% testing. All images underwent extensive preprocessing, including grayscale conversion, noise removal, and contrast-limited-adaptive-histogram equalization (CLAHE). All images were resized to 224 × 224 pixels. Using transfer learning, the baseline frozen layers were kept intact while the top layers were trained with a learning rate of 0.0001, which was tuned to the model’s requirements using early stopping to avoid overfitting. Results: With the outlined methodology, we obtained an astounding accuracy of 99.16%, including strong performance in the no-tumor category, where recall rates were approaching 100% and false positive rates were minimized. Conclusions: These findings strongly indicate that the application of lightweight convolutional neural networks in diagnostic imaging can considerably expedite accurate brain tumor identification by radiologists.

Skin Disease Detection Using CNN

Abstract: Globally, skin diseases affect many people. Treatment and care for these diseases can only be effective if detected early and accurately. Machine learning and computer vision have advanced to the point that convolutional neural networks (CNNs) are now powerful tools for automatically detecting skin diseases. A study on the application of CNNs to skin disease detection is presented, demonstrating the potential of deep learning algorithms to aid dermatologists and healthcare professionals. An image dataset containing images of various skin diseases is used in the proposed method, including dermatitis, eczema, psoriasis, and other common conditions. High-level representations are captured from input images using a pre- trained CNN (Convolutional Neural Network) model, such as VGG-16. For disease detection, these learned features are fed into a classification layer. The CNN model learns to identify patterns that correspond to the various diseases after being trained on a vast collection of skin photos. An image of skin disease is fed into the model, and the model is trained to extract features that can be used to build a classification model that can accurately identify the disease.. Key Words: Skin disease detection ,CNN, techniques Image processing, python, Deep learning

A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island

The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline.

Application of convolutional neural networks in the production and processing of flower and fruit tea

Application of convolutional neural networks in the production and processing of flower and fruit tea

Diagnosing Gingiva Disease Using Artificial Intelligence Techniques

Gingival and periodontal diseases, such as gingivitis and periodontitis, are critical public health concerns that can lead to severe complications if left untreated. Early and precise diagnosis is crucial to mitigate the progression of these conditions and improve oral health outcomes. This study investigates the application of convolutional neural networks (CNNs) in diagnosing gingival diseases using medical images, including X-rays and intraoral photographs. Several CNN architectures, including VGG16, Sequential CNN, MobileNet, InceptionV3, and suggestions for a voting method to enhance the prediction, were evaluated for their performance in classifying gingival conditions. MobileNet emerged as the most effective model, achieving a test accuracy of 92.73%; the suggested method relies mainly on its positive result. When the MobileNet's result is false, the process takes the voting result using the other methods. This boosts the accuracy to 96%. Surpassing other models in precision and recall metrics. Pre-processing techniques such as normalization using the CIELAB color space and data augmentation significantly enhanced model accuracy. The study employed robust evaluation methods, including 10-fold cross-validation and hyperparameter tuning, to ensure model reliability and generalizability. The findings highlight the transformative potential of AI-powered diagnostic tools in dental healthcare. By leveraging lightweight and efficient architectures like MobileNet, these tools can be deployed in resource-limited settings, offering real-time diagnostic support to healthcare professionals. Future work will focus on expanding datasets, exploring ensemble models, and improving interpretability to further enhance diagnostic accuracy and clinical applicability.

Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification

Brain tumor classification poses significant challenges in medical imaging, largely due to the heterogeneity and structural complexity of tumors. With Magnetic Resonance Imaging (MRI) serving as a cornerstone for diagnosis, manual interpretation by radiologists is time-consuming and prone to inter-observer variability. Recent advances in deep learning, particularly through the application of Convolutional Neural Networks (CNNs), have transformed medical image analysis by enabling automated, high-accuracy feature extraction. Despite their promise, the performance of CNNs is highly contingent upon optimal hyperparameter tuning, a process that can be both computationally demanding and pivotal for model efficacy. In this study, we employ Simulated Annealing (SA), a probabilistic metaheuristic technique, to methodically optimize the hyperparameters of a CNN architecture designed specifically for classifying brain tumors from MRI scans. Our approach employs a direct representation of hyperparameters alongside an efficient perturbation strategy, facilitating a comprehensive exploration of the parameter space. Experimental evaluations conducted on an extensive MRI dataset (N = 7023 scans classified into glioma, meningioma, no tumor and pituitary) demonstrate that our SA-optimized CNN model achieves a validation accuracy of 98.15%, thereby affirming the potential of SA in enhancing the performance of deep learning systems in medical diagnostics. These findings underscore the critical role of advanced hyperparameter optimization techniques in improving diagnostic accuracy and robustness, ultimately contributing to the development of more reliable and efficient brain tumor classification systems in clinical settings.

TSFANet: Trans-Mamba Hybrid Network with Semantic Feature Alignment for Remote Sensing Salient Object Detection

Recent advances in deep learning have witnessed the wide application of convolutional neural networks (CNNs), Transformer models, and Mamba models in optical remote sensing image (ORSI) analysis, particularly for salient object detection (SOD) tasks in disaster warning, urban planning, and military surveillance. Although existing methods improve detection accuracy by optimizing feature extraction and attention mechanisms, they still face limitations when dealing with the inherent challenges of ORSI. These challenges mainly manifest as complex backgrounds, extreme scale variations, and topological irregularities, which severely affect detection performance. However, the deeper underlying issue lies in how to effectively align and integrate local detail features with global semantic information. To tackle these issues, we propose the Trans-Mamba Hybrid Network with Semantic Feature Alignment (TSFANet), a novel architecture that exploits intrinsic correlations between semantic information and detail features. Our network comprises three key components: (1) a Trans-Mamba Semantic-Detail Dual-Stream Collaborative Module (TSDSM) that combines CNNs-Transformer and CNNs-Mamba in a hybrid dual-branch encoder to capture both global context and multi-scale local features; (2) an Adaptive Semantic Correlation Refinement Module (ASCRM) that leverages semantic-detail feature correlations for guided feature optimization; and 3) a Semantic-Guided Adjacent Feature Fusion Module (SGAFF) that aligns and refines multi-scale semantic features. Extensive experiments on three public RSI-SOD datasets demonstrate that our method consistently outperforms 30 state-of-the-art approaches, effectively accomplishing the task of salient object detection in remote sensing imagery.

Artificial Intelligence in Retinal Disease Detection: Technological Advances and Clinical Applications Research

Retinal lesions, as common complications of chronic diseases such as diabetes and hypertension, have become a leading cause of irreversible visual impairment worldwide. According to the 2023 report by the International Diabetes Federation (IDF), approximately 34% of diabetic patients globally develop diabetic retinopathy (DR), with a subset progressing to vision-threatening advanced stages. Traditional screening methods depend on eye doctors to manually analyze fundus images, which can be difficult due to issues like low accuracy, uneven access to medical resources, and differences in opinions among doctors. In recent years, breakthroughs in deep learning have provided a novel methodological framework for medical image analysis. In particular, the innovative application of convolutional neural networks (CNNs) in retinal image interpretation has enabled a paradigm shift from reliance on manual expertise to data-driven approaches. This study adopts a systematic literature review to summarize the technological advances of artificial intelligence in retinal disease detection and discusses the associated clinical challenges and future prospects. The findings reveal that CNN-based models, such as ResNet-50, achieve high accuracy rates of up to 94.2% in DR grading, significantly outperforming manual screening. Additionally, generative adversarial networks (GANs) and multimodal fusion strategies effectively enhance performance in small-sample settings and improve detection sensitivity. However, issues such as data heterogeneity and limited model interpretability continue to hinder clinical application. It is therefore imperative to promote large-scale deployment of AI-assisted diagnostic systems through the construction of standardized multi-center databases, development of lightweight models, and design of human-computer collaborative diagnostic interfaces.

Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets

Agriculture and its yields are indispensable to human life all over the planet. It is an essential part of many countries’ economies and without it the world’s population can not be fed. As such, guaranteeing harvest with minimal loss is a primary objective. One factor that heavily contributes to loss in crop harvesting are plant diseases, which often affect crops and their leaves. A plant’s leaf often carries symptoms that indicate whether or not a plant is infected, but traditional manual approaches to identifying these symptoms are tedious and laborious. Additionally the process of manually spotting diseases can be rather slow in a field where urgency and fast identification are very important. The sooner a disease gets identified, the sooner countermeasures can be carried out. To improve both the accuracy with which diseases can be recognized, as well as increasing the speed at which this can be carried out, deep learning methods have proven useful. Recently the field of plant disease recognition has seen a big uptick in the application of various convolutional neural network (CNN) models for the automatic classification of diseases. There exist many different highly-capable models at this time. There also exists a range of plant leaf disease classification image datasets containing different plants and diseases. However, there seems to be no consensus on which model is best suited to handle this task and the same can be said for the datasets. To the best of our knowledge, prior work has used a wide range of different models with different datasets in the way of feasibility studies, but without comprehensively identifying which models are best used in this field. In this work we test a large number of state-of-the-art CNN models on a wide range of openly available datasets to asses their performance and to identify models that are best suited for this field, in order to be able to built better models, and even new foundation models, based on these findings. 23 models have been tested on 18 datasets, both using transfer-learning and transfer-learning with additional fine-tuning added, for five iterations each. Transfer-learning allows models to utilize knowledge obtained from other previous tasks to be used for new tasks, reducing training time and lowering the need for training data. The experiments result in a total of 4140 having been trained for this work. All results will be compared and contextualized in order to find the best models architecture for plant leaf disease classification as well as assessing which datasets are well suited for this task.

Caries detection using deep learning and convolutional neural networks from radiographic images: A narrative review

Dental caries remains one of the most prevalent chronic diseases worldwide, posing significant public health and economic burdens. Early and accurate diagnosis is critical for effective management and prevention of complications. While traditional diagnostic methods such as clinical examinations and radiographic assessments are widely used, they suffer from limitations including inter-observer variability, low sensitivity in early detection, and subjectivity. The emergence of artificial intelligence (AI), particularly deep learning through convolutional neural networks (CNNs), offers promising advancements in caries detection from dental radiographs. This narrative review explores the application of CNNs in diagnosing dental caries using various imaging modalities, including bitewing, panoramic, and periapical radiographs. We summarize current evidence from key studies employing architectures such as ResNet, VGGNet, U-Net, and EfficientNet, demonstrating superior diagnostic accuracy, sensitivity, and specificity when compared to conventional approaches. CNN-based models enhance objectivity, reduce diagnostic time, and offer scalable integration into clinical workflows. However, challenges remain regarding dataset standardization, overfitting, model generalizability, and the lack of interpretability of AI decisions. The review also highlights limitations in image quality, annotation variability, and regulatory constraints hindering clinical deployment. Future prospects include the adoption of explainable AI (XAI), multimodal data integration, and the development of optimized CNN architectures tailored for dental applications. These innovations could lead to more transparent, robust, and widely accepted diagnostic tools in dentistry. In conclusion, CNN-based caries detection represents a transformative shift in dental diagnostics, enhancing precision, efficiency, and accessibility. Addressing current limitations through technical, ethical, and regulatory advancements is essential to harness the full potential of AI-driven diagnostics and improve global oral health outcomes.

Application of Convolutional Neural Networks in an Automatic Judgment System for Tooth Impaction Based on Dental Panoramic Radiography.

Background/Objectives: Panoramic radiography (PANO) is widely utilized for routine dental examinations, as a single PANO image captures most anatomical structures and clinical findings, enabling an initial assessment of overall dental health. Dentists rely on PANO images to enhance clinical diagnosis and inform treatment planning. With the advancement of artificial intelligence (AI), the integration of clinical data and AI-driven analysis presents significant potential for supporting medical applications. Methods: The proposed method focuses on the segmentation and localization of impacted third molars in PANO images, incorporating Sobel edge detection and enhancement methods to improve feature extraction. A convolutional neural network (CNN) was subsequently trained to develop an automated impacted tooth detection system. Results: Experimental results demonstrated that the trained CNN achieved an accuracy of 84.48% without image preprocessing and enhancement. Following the application of the proposed preprocessing and enhancement methods, the detection accuracy improved significantly to 98.66%. This substantial increase confirmed the effectiveness of the image preprocessing and enhancement strategies proposed in this study. Compared to existing methods, which achieve approximately 90% accuracy, the proposed approach represents a notable improvement. Furthermore, the entire process, from inputting a raw PANO image to completing the detection, takes only 4.4 s. Conclusions: This system serves as a clinical decision support system for dentists and medical professionals, allowing them to focus more effectively on patient care and treatment planning.

Characterization of subsurface anomalies in non-stationary thermal wave imaging using convolutional neural networks

Abstract An important development in the field of thermographic study is the application of convolutional neural networks (CNNs) to characterize subsurface abnormalities using quadratic frequency-modulated thermal wave imaging. This technology greatly improves the accuracy of identifying subsurface flaws by utilizing deep learning techniques, which makes it very useful for a variety of industrial applications cantered on material integrity evaluation. In this study, we present a new post-processing modality based on regression that is intended to efficiently characterize subsurface anomalies. Through experimental experiments involving carbon fibre reinforced polymers (CFRP) and glass fibre reinforced polymers (GFRP), our methodology has been rigorously confirmed. The purpose of the studies was to demonstrate the ability of our method to visualize subsurface structures, with a focus on precisely measuring their sizes. We evaluate our method’s efficacy by contrasting the outcomes with current methodologies, employing signal-to-noise ratio (SNR) as a crucial performance parameter. This assessment proves our method’s superiority in offering more trustworthy and lucid insights about underlying structures. In the end, our research adds to the expanding body of knowledge about non-destructive testing and has encouraging ramifications for material science and engineering applications in the future.