Maximally Stable Extremal Regions Research Articles

A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection

An intelligent transportation system (ITS) is a crucial element of a smart city, and it includes the capability to identify vehicle license plates. Utilizing digital image processing is a cost-effective method for identification. The tiny size of the number plate is just one of the many unfortunate difficulties with this approach. Hence, this research is crucial, particularly in enhancing the precision of detection. The Harris Corner approach is one way to locate the motor vehicle number plate location. However, the Harris corner method could be more optimal for analyzing moving vehicle videos as input. Since frame-by-frame variations in the video input's lighting, accuracy cannot be maximized. Furthermore, the vehicle and license plate appear significantly smaller due to the camera's distant positioning. Consequently, the authors suggest a hybrid approach using the Maximally Stable Extremal Regions (MSER) method. The Harris Corner and MSER methods will concurrently identify the initial position of the vehicle number plate. Moreover, the initial detection outcomes of the two techniques are compared and adjusted to achieve a more precise determination of the placement of the number plate. The results show that integrating the MSER method into the Harris Corner method yields a hybrid approach that enhances accuracy by 13%. Furthermore, it visually represents the selected number plate with greater accuracy.

License Plate Recognition Using Three-Dimensional Rotated Character Recognition and Instance Segmentation by Deep Learning

License plate recognition is currently used in various situations, such as parking lot vehicle management and the tracking of wanted vehicles. These applications require recognition from all possible camera angles. Therefore, we propose a method for license plate recognition in images captured from various camera angles. First, the license plate area is detected from the input image using the YOLOv5 object detection method. The subsequent process can be performed in two manners: by rotating the license plate to the front using projective transformation and trapezoidal correction, or by using graph matching. The first method performs Hough transforms or linear approximations as preprocessing, and then calculates the rectangle surrounding the license plate from the YOLOv5 output. After performing trapezoidal correction, the maximally stable extremal regions (MSER) are used to detect character candidates, and characters are recognized using 3D rotated character recognition. The second method performs character candidate detection and recognition without trapezoidal correction, followed by graph matching. These methods are versatile because they do not depend on the layout of license plates, which varies among countries and regions. Two types of datasets were used: a set of images containing Japanese license plates collected by ourselves, and a set of publicly available images containing Taiwanese license plates. Two recognition rates were evaluated: the license plate character recognition rate and license plate recognition rate in which all characters in one plate are recognized. The license plate character recognition rates using graph matching were 91.9% for the Japanese license plates and 89.3% for the Taiwanese license plates. The license plate recognition rates were 84.0% and 84.5%, respectively.

Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios.

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants.

An animal's locomotor rate is an important indicator of its motility. In studies of the nematode Caenorhabditis elegans (C. elegans), assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e. shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hu's moment invariants and a set of maximally stable extremal regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to a segmentation-based method that analyzes a worm's shape and a method based on video covariance, our technique is more robust to low image quality and background noise. We demonstrate the system's capabilities by testing the effects of serotonin and serotonin pathway mutations on C. elegans locomotor frequency.

Detection of COVID-19: A Metaheuristic-Optimized Maximally Stable Extremal Regions Approach

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants.

An animal's locomotor rate is an important indicator of its motility. In studies of the nematode C. elegans, assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e., shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hu's moment invariants and a set of Maximally Stable Extremal Regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to a segmentation-based method that analyzes a worm's shape and a method based on video covariance, our technique is more robust to low image quality and background noise. We demonstrate the system's capabilities by testing the effects of serotonin and serotonin pathway mutations on C. elegans locomotor frequency.

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures.

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

Fast eye centre localization using combined unsupervised technics

Eye movements offer precious information about persons? state. Video surveillance, marketing, driver fatigue as well as medical diagnosis assistance applications manage eye behavior. We propose a new method for efficiently detecting eye movement. In this paper, we combine circle eye model with eye feature method to improve the accuracy. A set of detectors estimate the eyes centers to increase the localization rate. As a pre-processing stage, the mean of the edges yields the center of the two eye regions. Image treatment operations reduce the ROI. A Circle Hough Transform (CHT) algorithm is adopted in a modified version as a detector to find the circle eye in the image; the circle center found represents the eye's pupil estimation. We introduced the Maximally Stable Extremal Region (MSER) as a second detector, which has never been used for eye localization. Invariant to continuous geometric transformations and affine intensity changes and detected at several scales, MSERs efficiently detect regions of interest, in our case eye regions, and precisely, their centers. Ellipses fit MSERs, and their centroid estimation match eyes center. We demonstrate that the true eye centers can be found by combining these methods. The validation of the proposed method is performed on a very challenging BioID base. The proposed approach compares well with existing state-of-the-art techniques and achieves an accuracy of 82.53% on the BioID database when the normalized error is less than 0.05, without prior knowledge or any learning model.

Deep learning enabled hemorrhage detection in retina with DPFE and splat segmentation in fundus images

The range of diabetics, hypertension, occlusions in vascular are rapidly increasing in the modern era. Adversarial effects of these diseases are the organ damage which is increasing from child to old age people. One of the severe damages involves the causes of eye diseases like diabetic eye disease or Diabetic Retinopathy (DR). Late prediction of DR may lead to permanent loss of vision. Hence there is a need among ophthalmologists in detecting and treating the eye diseases at the earliest stage. Detection of abnormalities as early as possible is a crucial task in today world as the existing strategies possess some setbacks. In this research work, a deep learning framework has been developed for the betterment in the prediction of retinal hemorrhage with the fundus image. The Double Pierced Feature Extraction (DPFE) is planned by merging the Enhanced Long Short-Term Memory (ELSTM) CNN and Maximally Stable Extremal Regions (MSER) algorithm. Splat algorithm is focused for segmentation which partitions a retinal image into various segments called splats of similar color, similar intensity and the spatial position. Every splat carries several details from that diverse features can be extracted. This splat based segment establishes a boundary based on an appropriate set of detailed information—needed features such as different area, filter, texture, splat related features and color etc. At last trained ELSTM with CNN is applied to predict the redder lesions of the fundus image, which confirms the presence of hemorrhage in the retina. The developed framework was evaluated using the DIARETDB2 dataset, and its performance was measured using parameters such as Classification Accuracy, Negative Acceptance Rate (NAR), Specificity, and Sensitivity and the experimental results highlight that the proposed methodology achieved an impressive sensitivity of 98.67% and specificity of 98.91%, outperforming existing techniques such as “ANFIS Classification with Cuckoo,” “ANFIS Classification with PSO,” and “Motion Pattern Recognition.” This analysis confirms the effectiveness of the proposed framework in accurately identifying relevant instances while excluding non-relevant patterns, showcasing its potential for improved pattern recognition and classification.

MuA-SAR Fast Imaging Based on UCFFBP Algorithm with Multi-Level Regional Attention Strategy

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.

MSER-Vertical Sobel for Vehicle Logo Detection

Detecting a vehicle logo is the first step before realizing the identity of the logo. However, the detection of logos can pose difficulties due to various factors, including logo variations, differing scales and orientations, background interference, varying lighting conditions, and partial obstruction. This paper presents a vehicle logo detection method using hand-crafted features. We used a combination of Maximally Stable Extremal Region (MSER) and Vertical Sobel. We combine vertical Sobel with MSER to overcome MSER's limitation in recognizing objects of different sizes. These two features are merged using a closing morphology operation to form blobs selected as logo candidate areas. Moreover, a Support Vector Machine (SVM) is implemented to choose a logo area by analyzing each candidate's Histogram of Oriented Gradient (HOG). The proposed method was compared with other methods by implementing them on the same dataset. The significant advantage of using MSER-Vertical Sobel is its fast computation time. It is faster than other approaches that use non-handcrafted features. The test results show that the MSER-Vertical Sobel can achieve high accuracy and the fastest computation time.

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt’s evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and maximally stable extremal regions (MSER), to enhance template-matching performance in e-learning. We systematically selected algorithms, integrated them into enhanced BBS schemes, and assessed their effectiveness against a baseline BBS approach using challenging data samples. A systematic algorithm selection process involving multiple reviewers was employed. Chosen algorithms were integrated into enhanced BBS schemes and rigorously evaluated. The results showed that the proposed schemes exhibited enhanced template-matching performance, suggesting potential improvements in personalization, engagement, and learning outcomes. Further, the study highlights the importance of robust template-matching feedback in e-learning, offering insights into improving educational quality. The findings enrich e-learning experiences, suggesting avenues for refining e-learning platforms and positively impacting the Egyptian education sector.

Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel® Core i7-8850H CPU running at 2.60 GHz.

EmotionFusion: A unified ensemble R-CNN approach for advanced facial emotion analysis

To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry.

The study of various registration methods based on maximal stable extremal region and machine learning

ABSTRACT This review article aims to discuss contemporary and traditional image registration methods, which involve establishing correspondences between two or more images of a same object. Two popular methods based on local features are maximal stable extremal region (MSER) and deep learning (DL). After evaluating various registration methods, it has been determined that MSER and DL algorithms are well organised, convenient and highly accurate. These methods can effectively handle different aspects of images, such as size and viewpoint, and are currently in trend for image and video registration due to their excellent repeatability, efficiency and feature extraction capabilities. The article encompasses a comprehensive analysis of approximately 35 registration algorithms based on MSER and DL, spanning 2004–2023. MSER and DL techniques can be modified to specific requirements at different stages of the registration process. A comparative study was also conducted to assess the advantages and disadvantages of implementing each unique registration mechanism.

Infrared small target detection using reinforced MSER-induced saliency measure

This paper presents a robust scheme to extract weak small target under intricate backgrounds. Firstly, the maximally stable extremal regions (MSER) algorithm is employed to seek extremal regions whose size and shape are consistent with the definition of small target and whose gray level is relatively stable. Then, in view of the fact that small targets are relatively sparse defect areas in the whole image, the MSER-induced global saliency measure (MGSM) is developed to reduce regular backgrounds and enhance target signal. Meanwhile, based on the characteristics of small targets with compact gray levels and a certain contrast with its surrounding background, the MSER-induced local saliency measure (MLSM) is designed to reliably enlarge the target signal and remove strong clutter interferences. Finally, the reinforced MSER-induced saliency measure (RMSM) defined by fusing MGSM and MLSM can successfully eliminate complex backgrounds and highlight real targets. Results demonstrate that this method has superiority in enhancing dim target against various backgrounds and has strong robustness to different target shapes and sizes.

Digital Image Text Recognition Using Machine Learning Algorithms

Abstract: Text recognition is used in various fields like document analysis, picture labeling, and text analysis, etc. The process of recognizing text from image is very crucial.. Employing Maximally Stable Extremal Regions (MSER) and Optical Character Recognition (OCR) algorithms have drastically changed the accuracy and reliability of text detection from photos. Our proposed work suggests a model for text recognition that makes use of both the MSER and OCR algorithms' benefits for the purpose of accuracy improvement and reliability of the digital text extraction procedure. OCR follows cutting-edge techniques to distinguish and identify individual characters, serving as the essential building block for text recognition. However, these algorithms may struggle when dealing with complex backdrops, blurry photos, or text that is structured in an atypical way. We use MSER approach, which excels at recognizing text sections by finding maximally stable regions across various severities and scales, to solve these constraints. The suggested model employs a multi-stage methodology. The MSER algorithm is used for extracting the likely text spots from the input image first. To boost OCR performance, these zones are then fine-tuned using pre-processing techniques including noise reduction and picture enhancement. The OCR system next processes the cleaned-up sections, identifying each region's text using machine learning and pattern recognition methods. The text that is recognized is then further processed to increase the accuracy and refine these findings. Thus, compared to most other models, the text recognition model that is built utilizing the MSER and the CNN (OCR, a component of CNN) algorithm performs better

Text Recognition from Images using CNN and MSER Algorithms

Abstract: The ability to recognize text from images is a great importance in a range of applications, including document analysis, images captioning and augmented reality. The reliability and accuracy of text extraction from images have been completely transformed by text recognition models using Optical Character Recognition (OCR) and Maximally Stable Extremal Regions (MSER) algorithms. In our study, we propose a text recognition model that leverages the advantages of both OCR and MSER algorithms to enhance the reliability and accuracy of the text extraction process. OCR algorithm serve as the fundamental basis for text recognition by utilizing advanced methods to separate and identify individual characters. None the less these algorithms can encounter difficulties when confronted with intricated backgrounds, images of poor quality, or text arranged in irregular layouts. To address these limitations, we integrate the MSER algorithm, which excels in detecting text regions by identifying maximally stable regions across different scales and intensities. Our proposed model follows a multi-stage approach. First, the input image, the MSER method is used to extract probable text locations. These regions are then refined using pre-processing techniques, such as noise removal and image enhancement, to improve OCR performance. Next, the refined regions are passed through the OCR algorithm, which utilizes machine learning and pattern recognition techniques to recognize the text within each regions. The recognized text is subsequently post-processed to refine the results and improve overall accuracy. The text recognition model is implemented using CNN (OCR which is part of CNN) and the Maximally stable Extremal regions (MSER) algorithms

Measurement of flank wear in turning of Monel K500 material based on novel image processing technique

In this article, a novel method for calculating the amount of flank wear that occurs during the turning of the nickel-based superalloy Monel K500 is presented. In order to determine how much flank wear the worn-out insert has sustained, a technique known as the Modified MSER (Maximally Stable Extremal Regions) was applied to it. In this particular piece of research, the algorithm known as the Modified MSER is referred to as the Flank Net. When the data generated by Flank-Net and the flank wear analysed by a tool maker's microscope were compared, an average percentage error of 4.038 percent was found to have been created by Flank-Net. As a result of the strong work-hardening tendency of the Monel K500 and the poor heat conductivity of the material, rapid tool wear has been observed. This has an influence on the dimensional and geometrical precision of the machined products. As a result, research was conducted to investigate the effect that the machining parameters have on tool wear. The experimental trials were navigated with the assistance of the L9 orthogonal array.

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

The use of drones and unmanned aerial vehicles has significantly increased in various real-world applications such as monitoring illegal car parking, tracing vehicles, controlling traffic jams, and chasing vehicles. However, accurate detection of license plate numbers in drone images becomes complex and challenging due to variations in height distances and oblique angles during image capturing, unlike most existing methods that focus on normal images for text/license plate number detection. To address this issue, this work proposes a new model for license plate number detection in drone images using Swin transformer. The Swin transformer is chosen due to its special properties such as higher accuracy, efficiency, and fewer computations, making it suitable for license plate number/text detection in drone images. To further improve the performance of the proposed model under adverse conditions such as degradations, poor quality, and occlusion, the proposed work incorporates a maximally stable extremal region-based regional proposal network to represent text data in the images. Experimental results on both normal license plates and drone images demonstrate the superior performance of the proposed model over state-of-the-art methods.