Image Processing Methods Research Articles

Evaluation of Asphalt Anti-Cracking Performance of SBS Polymer with SCB Method and Deep Learning

In recent years, there have been unprecedented developments in artificial intelligence. Object detection, voice recognition, face recognition etc. are some of the artificial intelligence applications. In this study, an auxiliary method for the automatic detection of cracks, one of the main deterioration problems on highways, is proposed. The crack formation of hot mix asphalts is investigated with an image processing method modeled with Attention SegNet architecture. Styrene-butadiene-styrene (SBS), the most widely used additive in bitumen modification, was used at 2%, 3%, and 4% ratios to modify 50/70 bitumen. Semi-circular asphalt specimens obtained with SBS modified bitumen were subjected to a semicircular bending (SCB) test and fracture performance was investigated. The effects of different temperature, notch size and additive on crack detection performance are evaluated. In the experimental study, maximum load, fracture energy, fracture toughness (KIC) values were obtained at low temperature, and resistance values against crack propagation were obtained by applying the J-integral method at intermediate temperature. The results demonstrated that with the addition of SBS, the fracture strength and maximum load values increased at each temperature value, with the 4% SBS mixture offering the highest performance. Moreover, the image segmentation performed with SegNet provided high accuracy and precision values for cracks. It was observed that the accuracy values of the image processing methods decreased at low temperature, while at high temperature, higher accuracy values were obtained as the cracking rate.

Non-Destructive Identification of Virgin Cashmere and Chemically Modified Wool Fibers Based on Fractional Order Derivative and Improved Wavelength Extraction Algorithm Using NIR Spectroscopy and Chemometrics

ABSTRACT Virgin cashmere is highly prized for its superior quality, and chemically modified wool is often used to fake it. However, traditional spectral analysis and image processing methods struggle to identify the two fibers. To address this, combining chemometrics with near-infrared (NIR) spectroscopy is proposed to identify virgin cashmere and chemically modified wool based on fractional order derivatives and improved wavelength extraction algorithm. The original spectra of the two fibers are very close, making identification difficult. Therefore, fractional order derivatives are used to amplify tiny spectral differences, which are often overlooked in conventional analysis. Meanwhile, common wavelength extraction algorithms few consider inherent relationship between spectral features and chemical properties, so an improved wavelength extraction algorithm using Shuffled Frog Leaping Algorithm (SFLA) and Beluga Whale Optimization (BWO) is employed to explore the connection of the two. The proposed method with Partial Least Squares Discriminant Analysis (PLS-DA) achieves an accuracy of 94.4%. Experimental results demonstrate that fractional order derivatives enhance tiny spectral features and differences, while extracted feature wavelengths correspond to main chemical characteristic bands of the fibers. This study shows the feasibility of identifying virgin cashmere and chemically modified wool and offers a novel idea for identifying fibers in the textile industry.

Digital Protection and Inheritance Path of Intangible Cultural Heritage based on Image Processing Algorithm

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm’s capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world’s rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives.

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.

An Efficient Algorithm for Automated Skin Lesion Detection: A Non-Machine Learning Approach

Skin lesion detection is a very important task in medical applications such as disease diagnostic and cancer detection. This paper suggests an efficient algorithm for skin lesion detection relying on advanced image processing techniques without using machine learning or deep learning algorithms. A set of algorithms is proposed to enhance traditional image processing methods to scrutinize dermatological images, concentrating on melanoma detection. The proposed method combines color space transformation, contrast enhancement, active contour segmentation. ABCD criteria (Asymmetry, Border irregularity, Color variegation, and Diameter) is used in feature extraction. The approach is evaluated on the ISIC (International Skin Image Collaboration) 2020 dataset, accomplishing an accuracy of 84% in differentiating between malignant and benign skin lesions. The results indicate the potential of non-machine learning techniques in dermatological image analysis, providing computationally efficient and interpretable alternative to data-intensive deep learning algorithms.

AIRBORNE LASER SCANNER AS A DATA SOURCE FOR BUILDING SELECTED ELEMENTS OF AN INTELLIGENT DATABASE FOR TRANSPORTATION

In this study, the main objective was to detect the road network and key road infrastructure elements based on airborne laser scanning data. The study included identification of the road network and determination of its axes using three independent methods, as well as detection of horizontal signs such as pedestrian crossings. The analysis process was based mainly on digital image processing methods, based solely on lidar data, without using information from other sources. The results of the analysis showed that the use of lidar data provides a fast and effective method for continuously updating information on road infrastructure and expanding the transportation database. This potentially opens the door to effectively updating relevant data in the area of transportation infrastructure.

Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.

An Overview of Handwritten Text Recognition Using Generative AI: Cutting-Edge Approaches, Key Challenges, and Future Directions

Recognizing handwritten text involves several stages, including data collection, data preparation, feature extraction, and categorization. It is essential for tasks like document processing, robotic automation, and historical document analysis, given the challenges of variations in size and shape. The survey focuses on enhancing the model's capabilities through data augmentation. Additionally, it delves into the intricate realm of recognizing handwritten numbers, presenting relevant work and emphasizing the use of various methods such as neural networks, k-nearest neighbor (KNN), and Support Vector Machine (SVM) for classifying handwritten numbers. The main goal of this research is to develop a technique using image processing methods to detect handwritten text within an image, addressing research gaps, challenges, and prospects in the field of text recognition.

Local Mean Suppression Filter for Effective Background Identification in Fluorescence Images.

We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.

Shear Performance in Reinforced Concrete Beams with Partial Aggregate Substitution Using Waste Glass: A Comparative Analysis via Digital Imaging Processing and a Theoretical Approach.

The usage of waste glass aggregate (WGA) associated with the replacement of fine aggregate (FA) and coarse aggregate (CA) is observed to reduce the number of raw materials for sustainable concrete. For this aim, a total of 15 beams were produced, and then investigational experiments were implemented to observe the shear performances. The stirrup spacing and WGA proportion were chosen as the main parameters. FA and CA were exchanged with WGA with weight proportions of 0, 10, and 20%. The experimental investigation results showed that changing stirrup spacing and WGA proportion affected the fracture and shear properties of reinforced-concrete-beams (R-C-Bs). Furthermore, the findings of the test results revealed that the proportion of WGA could be efficiently consumed as 20% of the partial replacement of FA. With the addition of FA to the mixture, the load carrying capacity of R-C-Bs increases. On the other hand, increasing the WGA ratio by more than 10% using CA, together with increasing the stirrup spacing, can significantly reduce the capacity of R-C-Bs. It was observed that the calculated shear strengths of R-C-Bs with inadequate stirrup spacing, based on ACI 318 and EC2 design codes, can be up to 52 and 79% higher than the experimental results for R-C-Bs containing coarse glass aggregate and 21 and 56% higher for R-C-Bs containing fine glass aggregate, respectively. Additionally, an image processing method was applied to describe the damages/microdamages in R-C-Bs. At that point, the findings obtained from the experimental part of the study were confirmed by the results of the image processing method. Although the strains obtained with the image processing method are reliable, it has not been determined exactly where the crack will occur due to the very sudden development of the shear crack at the moment of beam failure.

Prediction of Potato Volume by Neural Network Regression Model

Potatoes are essential for meeting the world's dietary requirements due to their high concentration of carbohydrates, vitamins, and minerals. Potato production is plentiful worldwide, but postharvest processing losses have reduced potato quality. Potatoes can be graded manually, which is time-consuming and labor-intensive. In this study, a neural network regression model was developed to predict the volume of potatoes using 2D images, which can be used as a parameter for grading potatoes automatically. The image processing method was used to extract features like the major axis, minor axis, and roundness of 400 potato samples, and each potato's volume was thoroughly measured using the water displacement method, and a dataset was generated. These attributes were used to create the neural network regression model and predict the volume. The results revealed that the mean squared error (MSE) was 83.93, the root mean squared error (RMSE) was 9.16, and R2 was approximately 0.882. The potatoes were categorized into three groups: small, medium, and large, based on their size. The utilized model demonstrated an overall accuracy rate of 91.4%. Specifically, it achieved 90% and 85% accuracy for small-sized and medium-sized potatoes, respectively, whereas the model displayed a decrease in accuracy from 60% to 70% for large-sized potatoes. To summarize, the suggested neural network regression model provided an efficient approach to volume prediction using 2D images to improve the grading process of agricultural products.

Plant Leaf Disease Detection Using Xception Model

The traditional farming practices have been causing significant financial losses to farmers due to various reasons. However, the implementation of a modern, smart agricultural system utilizing machine learning techniques appears promising in safeguarding farmers and traders against these risks. This advanced system facilitates farmers in identifying common diseases through simple image recognition, employing a variety of image processing methods. Notably, the Convolutional Neural Network (CNN) algorithm stands out as an effective choice among these methods. Interestingly, among the available models, there has been limited utilization of the Xception model, and no comprehensive comparative study involving this model with different classifiers was found. To address this gap, a study was undertaken in two distinct approaches. Firstly, the Xception model demonstrated remarkable accuracy in detecting plant diseases, achieving an impressive 98.3 percent accuracy rate. In comparison, other classifiers such as logistic regression and three additional methods attained accuracy rates of 93 percent and 92 percent, respectively. Secondly, a comparative analysis was conducted on the top 12 papers out of a selection of 45 papers, each employing different methods. The Xception method once again proved to be effective in this context. Through these tests and review studies, the Xception method emerged as a reliable and superior choice. It is expected that this research will provide valuable insights for researchers and stakeholders, potentially guiding the development of new research initiatives in this field.

Faster R-CNN-CA and thermophysical properties of materials: An ancient marquetry inspection based on infrared and terahertz techniques

The demand for non-invasive inspection (NII) is ever-increasing in the field of cultural heritage conservation. NII is a two-step procedure, first of data acquisition and second of defect detection. Stand-alone imaging techniques such as infrared thermography (IRT) are often insufficient for performing a complete remote analysis and diagnosis of historic structures and art pieces that are of very high cultural value. On this point, an emerging optical inspection method, terahertz time-domain spectroscopy (THz-TDS), is herein employed to provide more details of deeper defects. The imaging results from THz-TDS and IRT are compared and analyzed by employing advanced image processing methods. Next, to achieve automatic inspection of the test sample, which is an ancient marquetry, a Faster R-CNN with coordinate attention (Faster R-CNN-CA) is proposed and fitted with data from two different sources. Worth noting is that, in order to populate sufficient data for training, samples are simulated using finite element analysis and finite difference time domain method. The experiments demonstrate that the mean average precision of the Faster R-CNN-CA model improves by 6.09% over the traditional Faster R-CNN model.

An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection

Pediatric pneumonia remains a significant global threat, surpassing all other communicable diseases in child mortality and necessitating rapid diagnosis. UNICEF identifies it as a primary cause of death in children under five. However, traditional image processing methods are time-consuming and struggle to capture distinct features due to low-intensity radiation in chest radiographs. Furthermore, skewed training data complicates the extraction of reliable and relevant features. To this end, we propose a novel EXplainable Contrastive-based Dilated Convolutional Network with Transformer (XCCNet) for pediatric pneumonia detection. XCCNet harnesses the spatial power of dilated convolutions and the global insights of contrastive-based transformers for effective feature refinement. A robust chest X-ray processing module tackles low-intensity radiographs, while adversarial-based data augmentation mitigates data distribution challenges. Furthermore, we actively integrate an explainability approach through feature visualization, directly aligning it with the attention region that pinpoints the presence of pneumonia or normality in radiographs. We comprehensively evaluate the efficacy of XCCNet on four publicly available datasets: Kermany, VinDR-PCXR, NIH-Pediatric, and Trivedi, achieved accuracies of 99.76%, 91.56%, 92.87%, and 97.19%, respectively. Quantitative and qualitative assessments demonstrate the effectiveness of XCCNet capabilities. XCCNet’s superior performance enhances the early diagnosis of pneumonia, potentially saving many children’s lives.

Non-destructive testing based on Unet-CBAM network for pulsed thermography

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.

Effective removal of global tilt from atomically-resolved topography images of vicinal surfaces with narrow terraces

The main feature of vicinal surfaces of crystals characterized by the Miller indices (hhm) is rather small width (less than 10 nm) and substantially large length (more than 200 nm) of atomically-flat terraces. This makes difficult to apply standard methods of image processing and correct visualization of crystalline lattices at the terraces and multiatomic steps. Here we consider two procedures allowing us to minimize effects of both small-scale noise and global tilt of sample: (i) analysis of the difference of two Gaussian blurred images, and (ii) subtraction of the plane, whose parameters are determined by optimization of the histogram of the visible heights, from raw topography image. It is shown that both methods provide nondistorted images demonstrating atomic structures on vicinal Si(556) and Si(557) surfaces.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Modeling the Change in Amino Acid and Peptide Contents during the Aging of Bottled-Fermented Sparkling Wines by Image Processing Methods

In this study, natural sparkling wine was produced using a mixture (1:1) of Emir and Dimrit grapes harvested (Around the Urgup District of Turkey). The change of free amino acid and peptide contents in sparkling wine depending on the aging time and yeasts (free and immobilized forms of the Saccharomyces bayanus and Saccharomyces oviformis) used in the second fermentation stage were examined by image processing methods. The immobilized yeasts were determined as more than the free ones along the second fermentation. It was also determined that the yeast type does not have a significant effect on both the peptides and free amino acid contents of sparkling wines and these contents were divided into 3 classes from high to low by measuring separately along the aging time. The amino acid contents of sparkling wines reached the highest level between the 335th and 365th days of aging time and also exceeded the amino acid contents of base wine.

An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network.

Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.

A general framework to optimize fracture parameters in tight gas reservoir of heterogeneous channel sand bodies

Optimizing fracture parameters can unlock the potential of tight gas reservoirs. Various frameworks have been proposed as efficient optimization tools. However, current frameworks are mostly aimed at regular rectangular oil or gas reservoirs. The effect of heterogenous channel sand bodies on fracture parameter optimization has not been discussed. Here, we proposed a general optimization framework. The first step is to establish a heterogeneous channel sand bodies model based on image processing and data interpolation methods. The second step is to couple optimization algorithm and reservoir simulator to optimize fracture parameters. Then the framework is applied to two actual wells in Shaximiao Formation in Sichuan Basin in China. Results show that, the effect of channel sand body on the fracture optimization cannot been ignored. The optimal fractures tend to be concentrated in areas with good physical properties and large control areas. The sand body boundary has a significant control effect on the fracture half-length. For both fracture length and conductivity, double dumbbell shape is better than uniform shape. Our general framework serves as an efficient tool to optimize fracture design in heterogeneous channel sand body, which plays a growing role in enhancing tight gas recovery.