Edge Detection Research Articles

Detection of Piston Ring Deficiency in The Assembly of Automotive Ball Joint and Tie Rod End Parts

In today's intensely competitive environment, businesses strive to optimize production efficiency to reduce costs, increase profitability, and ensure customer satisfaction. This focus on efficiency and quality enables businesses to operate more effectively, gain a competitive advantage in the market, and move towards sustainable growth. This study uses image processing techniques to detect missing segments in the assembly of ball joints automatically. In the automotive industry, performing quality control of critical components before assembly and detecting and classifying the defective ones is essential. Many quality control methods can be applied with existing technologies. This paper proposes an automatic real-time control based on image processing techniques to detect ball joint missing segments, a common defect in the automotive industry. In the company, operators perform defect detection by visual inspection. In this system, production continues in cases where the operator cannot detect the defect. This system aims to detect the errors made by the operator during the assembly operations and provide instant feedback. The developed system uses OpenCV library algorithms that are highly accurate in detecting defects in manual assembly processes so that missing components are removed from the production chain, and production quality is significantly improved. Accuracy is over 94% when identifying missing segments, about 30% better than traditional methods. In tests, 1200 ball joints were run through the system, resulting in 1150 defects being correctly identified and removed from the production line. Accuracy is high thanks to the application of various image processing techniques such as grayscale conversion, edge detection, and shape recognition. This also provides real-time feedback to the operator so the system can reduce detection and response time from 15 seconds to 5 seconds. This increases production speed and reduces the error rate in manual assembly processes by 20%. This paper also highlights the potential of image processing technology in manufacturing. It will contribute to improved quality control mechanisms to increase the reliability and efficiency of production lines in the automotive industry.

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Development and modification Sobel edge detection in tuberculosis X-ray images

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.

Folk art classification using support vector machine

<p dir="ltr">Tremendous amounts of effort have been carried out every year by the governments of all the countries to preserve art and culture. Art in the form of paintings, artifacts, music, dance, and cuisines of every country has the utmost importance. The study of Tribal arts provides deep insight into our history and acts as a milestone in the roadmap of our future. This paper focuses on three popular folk arts namely: Gond, Manjusha, and Warli. 300 images of each artwork have been collected from various online repositories. To generate a robust system, data augmentation is applied which results in 7510 images. A feature vector based on a generalized co-occurrence matrix, local binary pattern, HSV histogram, and canny edge detector is constructed and classification is performed using a linear support vector machine. 10- fold cross-validation produces 99.8% accuracy.</p>

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security

Biometric systems have gained attention as a more secure alternative to traditional authentication methods. However, these systems are not without their technical limitations. This paper presents a hybrid approach that combines edge detection and segmentation techniques to enhance the security of cloud systems. The proposed method uses iris recognition as a biometric paradigm, taking advantage of the iris' unique patterns. We performed feature extraction and classification using hamming distance (HD) and convolutional neural networks (CNN). We validated the experimental findings using various datasets, such as MMU, IITD, and CASIA Iris Interval V4. We compared the proposed method's results to previous research, demonstrating recognition rates of 99.50 % on MMU using CNN, 97.18 % on IITD using CNN, and 95.07 % on CASIA using HD. These results indicate that the proposed method outperforms other classifiers used in previous research, showcasing its effectiveness in improving cloud security services.

Integrated photonic fractional convolution accelerator

An integrated photonic circuit architecture to perform a modified-convolution operation based on the discrete fractional Fourier transform (DFrFT) is introduced. This is accomplished by utilizing two nonuniformly coupled waveguide lattices with equally spaced eigenmode spectra, the lengths of which are chosen so that the DFrFT and its inverse operations are achieved. A programmable modulator array is interlaced so that the required fractional convolution operation is performed. Numerical simulations demonstrate that the proposed architecture can effectively perform smoothing and edge detection tasks even for noisy input signals, which is further verified by electromagnetic wave simulations. Notably, mild lattice defects do not jeopardize the architecture performance, showing its resilience to manufacturing errors.

Overlapping Shoeprint Detection by Edge Detection and Deep Learning.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional convolutional neural networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces and employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

Ricci curvature based volumetric segmentation

The level set method has played a critical role among many image segmentation approaches. Several edge detectors, such as the gradient, have been applied to its regularisation term. However, traditional edge detectors lack high-order information and are sensitive to image noise. To tackle this problem, we introduce a method to calculate the Ricci curvature, a vital curvature in three-dimensional Riemannian geometry. In addition, we propose incorporating the curvature into the regularisation term. Experiments suggest that our method outperforms the state-of-the-art level set methods and achieves a comparable result with the Swin UNETR and Segment Anything.

Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network.

To detect foot ulcers in diabetic patients by analysing thermal images of the foot using a deep learning model and estimate the effectiveness of the proposed model by comparing it with some existing studies. Open-source thermal images were used for the study. The dataset consists of two types of images of the feet of diabetic patients: normal and abnormal foot images. The dataset contains 1055 total images; among these, 543 are normal foot images, and the others are images of abnormal feet of the patient. The study's dataset was converted into a new and pre-processed dataset by applying canny edge detection and watershed segmentation. This pre-processed dataset was then balanced and enlarged using data augmentation, and after that, for prediction, a deep learning model was applied for the diagnosis of an ulcer in the foot. After applying canny edge detection and segmentation, the pre-processed dataset can enhance the model's performance for correct predictions and reduce the computational cost. Our proposed model, utilizing ResNet50 and EfficientNetB0, was tested on both the original dataset and the pre-processed dataset after applying edge detection and segmentation. The results were highly promising, with ResNet50 achieving 89% and 89.1% accuracy for the two datasets, respectively, and EfficientNetB0 surpassing this with 96.1% and 99.4% accuracy for the two datasets, respectively. Our study offers a practical solution for foot ulcer detection, particularly in situations where expert analysis is not readily available. The efficacy of our models was tested using real images, and they outperformed other available models, demonstrating their potential for real-world application.

Road Sense: Intelligent Road Monitoring System

Abstract: This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.

Unearthing Egypt’s Golden Legacy: Geophysical Insights and New Opportunities in the Central Eastern Desert

Gold mining in Egypt’s Central Eastern Desert (ECED) has a rich history dating back to the Old Kingdom period. In recent years, there has been renewed interest from international mining companies, and several potential areas for gold mining have been identified. Extensive studies have been conducted on the deposition and occurrence of gold in the region, identifying over 100 areas of deposition. Aeromagnetic and radioactive surveys have played a crucial role in locating gold occurrences in ECED by utilizing data from these techniques to identify potential gold deposits. This study utilized geophysical data, including total aeromagnetic intensity (TM) and radiometric data, to identify hydrothermal zones and locate potential areas of gold occurrence. The radiometric ternary map was used to refine the search for gold deposits. Magnetic data were analyzed using edge detection tools to determine the structural framework of the area, facilitating the identification of regions with a high potential for gold occurrence. By integrating these geophysical datasets, this study provided a comprehensive understanding of the geological features and potential for gold mineralization in the study area. The area is divided into four sections by three shear zones, with high magnetic anomalies observed in the southeastern part. The radiometric data revealed that gold occurrence is associated with hydrothermal alteration zones, identified using the K/Th ratio and radiometric ternary map. However, not all these zones contain gold, and the area’s structure and magnetic suitability must be considered when selecting suitable locations for gold extraction. The study area presents a promising opportunity for gold exploration, with the integration of edge detection and radiometric analysis being crucial in identifying suitable locations for exploration.

Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

Statistical analysis of low latitude spread F at the American, Atlantic, and Pacific sectors using digisonde observations

Statistical analysis of low latitude spread F is presented for three different longitudinal sectors from Jicamarca (12°S, 76.8°W, −2.5° declination angle) from 2001 to 2016, Ascension Island (7.9°S, 14.4°W, −15.09° declination angle) from 2000 to 2014, Kwajalein (8.71°N, 167.7°E, 7.5° declination angle) from 2004 to 2012. Digisonde data from these stations have been processed and analyzed to study statistical variations of equatorial spread F, a diagnostic of irregular plasma structure in the ionosphere. A new automated method of spread F detection using pattern recognition and edge detection for low latitude regions is used to determine solar and seasonal variation over these three sites. An algorithm has been developed to detect the foF2 and hpF2 parameters and this has been validated by comparisons with manually scaled data as well as with SAMI2 and International Reference Ionosphere models showing good correlation. While significant variation is not observed over the solar cycle, the different longitudes and declination angles contribute to the variations over the seasonal cycle.

Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation

Elm (Ulmus pumila L.) sparse forest plays an vital role in maintaining local ecological stability and security in the Otingdag Sandy Land area. Prior studies on elm canopy extraction have predominantly relied on manual parameter configuration, resulting in unsatisfactory levels of generalization. To meet the needs of high-precision and rapid recognition of elm sparse forests in large areas, this study proposed a recognition method for elm sparse forest that orients to high spatial resolution remote sensing imageries, using deep-learning-based semantic segmentation techniques. It can automatically learn features that are conducive to segmenting the canopy of elm trees, and retains good generalization ability on the Gaofen-2 imageries obtained in different regions. First, we constructed a dataset specialized for elm canopy semantic segmentation task, and annotated over 130,000 elm canopies based on Gaofen-2 imageries. In addition, we trained 7 deep-learning semantic segmentation model candidates. Among them, MANet showed the best performance, with its F1-score reaching 81.44%. Lastly, we applied edge detection to the elm canopy coverage area, and automatically extract the elm canopy. The proposed method can provide technical support for the investigation and monitoring of elm sparse forests, while facilitates local desertification prevention efforts in the entire Otingdag Sandy Region.

Transformer-based berm detection for automated bulldozer safety in edge dumping

Ensuring the safety of automated bulldozers during edge dumping operations in open-pit mines requires accurate detection of the berm's edge. However, precisely detecting berm edges in open-pit mining scenes is challenging due to the presence of similar texture structures. To address this issue, this paper created a dataset for berm edge detection that covers multiple scenes and proposed the edge detection model with transformer (MEDTER). This model, based on a transformer with a two-stage detection structure, aims to restore thin edges. Additionally, the deep multilevel aggregation decoder enhances the information flow in the transformer to improve the sensitivity to detailed information. The BCE-Dice loss functions effectively handle the problem of pixel imbalance. Results demonstrate that the proposed model achieves significant improvements compared to others. Furthermore, the model exhibits excellent detection performance despite variations in scenes and detection distances, suggesting its practical value in ensuring the safety of automated bulldozers during edge dumping.

Rapid post-disaster assessment of residential buildings using Unmanned Aerial Vehicles

Disasters impose significant costs on the built environment due to extensive damages. Accelerated by climate change, disasters such as hurricanes have recently become more frequent and destructive, underscoring the need for rapid post-disaster assessments toward swift search and rescue efforts and damage evaluations. In this regard, this study proposes an image-processing-based method employed on Unmanned Aerial Vehicle (UAV) imagery for automatic assessment of post-disaster damaged buildings. The proposed approach integrates texture-based features, including dissimilarity and homogeneity, along with edge-based features, for which Canny edge detection is employed. The edge-based features introduce novel indices representing the level of irregularity by assessing the entropy as well as the uniformity in the distribution of edge angles. The proposed features are fed into a Naïve Bayesian Classification process to classify damaged and undamaged classes, which makes it possible to account for the underlying uncertainties. The proposed method demonstrates a validation accuracy of 89.3 percent using real-life post-disaster images, effectively distinguishing between damaged and undamaged houses. The findings underscore the potential of employing UAV-captured images and advanced image processing techniques for rapid and accurate post-disaster damage assessment.

System Design of Ocean Temperature Measurement System Using Brillouin Lidar Based on Dual Iodine Cells

Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar technique is the only way to carry out seawater temperature profile measurements over large areas. However, it is insufficient for measuring speed, the receiving field, stability, spectral integrity, simple system structures, and so on. Therefore, we propose a Brillouin lidar method combining two iodine cells at different temperatures to realize temperature measurements, where one iodine cell is used as a filter to absorb the elastic scattering and the other as an edge detection discriminator to obtain the seawater temperature measurement. The system has a fast measurement speed, a large receiving field, a simple system structure, and high stability. The system feasibility was verified via principle simulation and real iodine absorption curve measurements. For an ocean temperature of [5 °C, 15 °C], a laser wavelength of 532.10495 nm was more appropriate, corresponding to the iodine pool temperature combinations of 50 °C and 78 °C. For an ocean temperature of [15 °C, 32 °C], a laser wavelength of 532.10518 nm was more appropriate, corresponding to the iodine cell temperature combinations of 60 °C and 78 °C. When the laser intensity reached a measurement precision of 1‰, the temperature could be predicted with an accuracy of up to 0.2 K. This work shows promise as a potential solution for seawater temperature profile measurement.

Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.

Fine interpretation of magnetic data for a concealed mineral deposit: A case study of the Fe-polymetallic deposit from Western China

We interpret the magnetic anomalies for exploration and characterization of the Galinge iron polymetallic deposit, which is the largest iron polymetallic deposit in Western China. This undeveloped deposit is completely concealed by the thick Quaternary sediments, presenting an exploration challenge. A lot of drillholes have been implemented focusing on the strong magnetic anomaly centers, while the relationship between magnetite-bearing and non-magnetite-bearing drillholes is unclear, and the metallogenic regularity in the whole area needs to be further studied. The edge detection is applied on the downward continued field to resolve individual magnetite ore bodies and enhance possible concealed ore bodies. Three anomaly belts trending NE-SW with a total extension length of ∼11 km are first revealed. Combining the sharper images from the enhanced magnetic anomaly and the drillholes, we infer that the deposits would be related to the syncline structure. Forward modeling confirms the inferred three belts of the ore bodies and also shows possibly two layers deposits in which the deep layer would be located at a depth of ∼1000 m. Although the deeper deposit results in a very smooth and low amplitude magnetic anomaly, the forward modeling shows that the anomalies from both the shallow magnetite and the inferred deep one fit the observed anomaly better. One deep drillhole reveals the fact that the magnetite is existed at a depth of ∼1000 m which highlights the interpretation of the deep deposit. The results show the contribution of the fine processing techniques which would be used to distinguish zones of potentially concealed mineralization.