Edge Detection Algorithm Research Articles

AngioPy: an open-source, deep learning-driven tool for coronary artery segmentation

Abstract Background Quantitative coronary angiography (QCA) permits the objective assessment of coronary artery disease. However, traditional edge detection algorithms often struggle with accurate vessel segmentation due to the complexity and variable quality of angiographic images. As a result, manual correction is frequently needed, which is a major limitation of current QCA systems. Purpose We developed angioPy, a deep learning-driven tool for vessel segmentation that employs user-defined ground truth points to minimise manual correction. We compared its performance without correction with an established QCA system. Methods angioPy was developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. Deep learning models were constructed using the U-Net architecture with one of three popular backbones for image classification: ResNet101, DenseNet121, and InceptionResNet-v2. Critically, the model was constructed to integrate the user’s clinical expertise through the selection of a 5-10 ground-truth points along the length of the vessel of interest including the desired start and end point of vessel segmentation. These ground truth points were added to a separate channel of the input image. Five-fold cross validation was performed using proportions of 3:1:1 for training, validation, and test sets, respectively. External validation was performed on a separate dataset of 580 images not used for model development. For both datasets, ground truth segmentation masks were provided by interventional cardiologists using specialised in-house software. A separate analysis was performed in a cohort of 74 patients with mild/moderate stenoses that underwent vessel segmentation using both angioPy and an established commercial QCA package (Medis QFR®). Vessel dimensions at stenoses (proximal and distal reference, minimal luminal diameter) were extracted from both segmentation approaches and compared (609 diameters in total). Results The best performing model performed segmentation with an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2% of masks exhibiting an F1 score > 0.8 (Figure 1). Simliar results were achieved in the external validation set (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Stenosis dimensions measured with angioPy (mean diameter 3.54 ±1.15 mm) exhibited excellent agreement with QCA (r=0.95 [95% CI 0.95-0.96], p<0.001; mean difference -0.18 mm [limits of agreement: -0.84 to 0.49]) (Figure 2). Minimal luminal diameter (mean 2.93 ±0.91 mm) also exhibited strong agreement (r=0.93 [95% CI 0.91-0.95], p<0.001; mean difference -0.06 mm [limits of agreement: -0.70 to 0.59]. Conclusion angioPy performs rapid and accurate coronary segmentation without the need for manual correction. Available as an open-source tool, angioPy has the potential to increase the efficiency and reliability of QCA.Figure 1.angioPy segmentationFigure 2.angioPy vs QCA (Medis)

FPGA implementation of sobel edge detection algorithm

Sobel Edge detection algorithm is used to extract the edges (region of maximum variation) from an image. It is based on the concept that the edges of an image contains maximum information whose computation depends on multipliers and square root. As multipliers consume more logic, a modified sobel edge detection algorithm which does not employ multipliers and square root function is proposed. A mathematical model of the proposed sobel edge algorithm was first developed and MATLAB was used to verify the model. On comparing with the original model, the proposed model has a SSIM of 96.43%. To analyse the hardware complexity, Verilog model of the modified sobel edge detection algorithm was developed using Quartus II. The chosen evaluation board is Cylone III FPGA EP3C120F780. The performance metrics such has Logic Elements utilization, Power dissipation and Maximum Operating Frequency were obtained. Open-Source toolchain (Yosys, OpenVPR, and Google Skywater 130nm PDK) was used to obtain the RTL Netlist and Synthesis reports. Verilog Modules for the Camera (CMOS OV7670) interface and FIFO Buffer were synthesized. The modified algorithm was integrated with them. An HSMC (HSMB) breakout board was connected to the FPGA Development board to increase the number of I/O ports. Thus in real time, the proposed modified Sobel Edge detection system can be used as a pre-processor to reduce the amount of computations and power consumption.

Retraction Note: Visual communication of moving images based on AI recognition and light sensing image edge detection algorithm

Retraction Note: Visual communication of moving images based on AI recognition and light sensing image edge detection algorithm

Non-contact measurement of structural dynamic displacement based on improved edge detection and video interpolation

In structural health monitoring (SHM), smartphones are increasingly used for non-contact dynamic displacement measurement using computer vision. To overcome limitations in capturing high-frame-rate data, a non-contact measurement method of structural dynamic displacement based on an improved edge detection algorithm and video frame interpolation is proposed. Initially, a smartphone is employed to capture the structure’s vibration video and then generates intermediary frames between adjacent frames that match the structure’s motion state by using a video frame interpolation algorithm to obtain a video with a high frame rate. Subsequently, the improved edge detection algorithm is applied to measure the displacement within the interpolated video. In order to address the problem that traditional edge detection may have false edges leading to the degradation of measurement accuracy, a feature point tracking technique is proposed based on the traditional edge detection technique, which effectively excludes the interference of false edges in ROI and improves the accuracy of displacement monitoring. Experimental validation on a cantilever beam and steel frame bridge demonstrates the method’s viability and precision, highlighting its effectiveness in improving displacement measurement accuracy compared to traditional methods.

A novel data-driven state evaluation approach for photovoltaic arrays in uncertain shading scenarios

Accurately determining the operating state of photovoltaic (PV) power-generation equipment and providing accurate support for maintenance management requires evaluation of the states of PV arrays under partial shading conditions. Results of such evaluation enable the optimization of maintenance measures to reduce maintenance costs and extend the lifespan of PV modules, thereby increasing the overall revenue of the PV power station. Therefore, this study proposes a novel state-evaluation approach based on current-voltage (I-V) curves under partial shading conditions. Based on the size of the pixel values in this curve, the proposed novel state-evaluation was performed using the proportion of the values of the normal and shaded curves under the same environmental conditions. Specifically, the feature variables (open-circuit voltage, short-circuit current) and pixel values within the I-V curve were analyzed and calculated using a sensitivity algorithm and computer vision algorithms (Canny edge-detection algorithm and Green's theorem). Based on the mapping relationship between feature variables and pixels, an improved Gaussian-process regression method with a combined kernel function (dot product and Matérn function) was proposed to predict the pixel values under the corresponding normal curve. This combined kernel function effectively captured causal relationships and handled outliers in the test dataset to improve the robustness of the model. Finally, the ratios of the pixel values were calculated using the predicted pixels within the normal curve and the calculated pixels within the shading curve. Experiments demonstrated that the root mean square error (RMSE), mean absolute error (MAE), R squared (R2) and log likelihood of the improved Gaussian-process regression method reached 0.011, 0.0086, 0.9996, and 24.7096, respectively.

AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

BackgroundQuantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g. angiography-derived fractional flow reserve).We developed AngioPy, a deep-learning model for coronary segmentation that employs user-defined ground-truth points to boost performance and minimise manual correction. We compared its performance without correction with an established QCA system. MethodsDeep learning models integrating user-defined ground-truth points were developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. External validation was performed on a dataset of 580 images. Vessel dimensions from 203 images with mild/moderate stenoses segmented by AngioPy (without correction) and an established QCA system (Medis QFR®) were compared (609 diameters). ResultsThe top-performing model had an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2 % of masks exhibiting an F1 score > 0.8. Similar results were seen with external validation (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Vessel dimensions from AngioPy exhibited excellent agreement with QCA (r = 0.96 [95 % CI 0.95–0.96], p < 0.001; mean difference − 0.18 mm [limits of agreement (LOA): −0.84 to 0.49]), including the minimal luminal diameter (r = 0.93 [95 % CI 0.91–0.95], p < 0.001; mean difference − 0.06 mm [LOA: −0.70 to 0.59]). ConclusionAngioPy, an open-source tool, performs rapid and accurate coronary segmentation without the need for manual correction. It has the potential to increase the accuracy and efficiency of QCA.

Unbalanced Data-Based Fault Diagnosis Method of Bearing Utilizing Time-Frequency DCGAN Processing

Aiming at the unbalanced datasets of bearing fault samples, a fault diagnosis method based on time-frequency conversion and processing of bearing vibration signals based on deep convolutional generative adversarial network (DCGAN) is proposed in this paper. Firstly, through short-time Fourier transform(STFT), the vibration signals are converted into time-frequency images, and then time-frequency images are input into DCGAN to expand the fault samples.Secondly, the expanded fault samples are evaluated for image quality through the comprehensive method of peak signal-to-noise ratio(PSNR) and structural similarity(SSIM). Thirdly, using the Canny edge detection algorithm to extract features of the time-frequency image, and using the obtained binary image as the feature.Finally, k-nearest neighbor algorithm is used for classification to testify the superiority of time-frequency DCGAN processing. The experimental results show that the expanded samples can effectively improve the imbalance of the samples and improve the accuracy of fault diagnosis of bearing.

Defect Detection of GFRP Composites through Long Pulse Thermography Using an Uncooled Microbolometer Infrared Camera.

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites.

Fast and accurate distal locking of interlocked intramedullary nails using computer-vision and a 3D printed device

IntroductionDistal locking is a challenging and time-consuming step in interlocked intramedullary nailing of long bone fractures. Current methods have limitations in terms of simplicity, universality, accuracy, speed, and safety. We propose a novel device and software for distal locking using computer vision.Methods and materialsThe device consists of an universal ancillary clamp, a telescopic arm, a viewfinder clamp, and a radio-opaque cross. The software uses a camera photo from the C-arm intensifier and adjusts for geometric projection deformities. The software employs edge detection, Hough transform, perspective interpolation, and vector calculation algorithms to locate the distal hole center. The device and software were designed, manufactured, and tested using 3D CAD, FEM, DRR, and performance testing on phantom bones.ResultsThe device and software showed high accuracy and precision of 98.7% and 99.2% respectively in locating the distal hole center and calculating the correctional vector. The device and software also showed high success ratio in drilling the hole and inserting the screw. The device and software reduced the radiation exposure for the surgeon and the patient. The success ratio of the device and software was validated by the physical testing, which simulated the real clinical scenario of distal locking. The radiation exposure was as low as 5 s with a radiation dose of 0.2mSv, drastically reducing radiation exposure during distal locking.DiscussionOur device and software have several advantages over other distal locking methods, such as simplicity, universality, accuracy, speed, and safety. Our device and software also have some disadvantages, such as reliability and legislation. Our device and software can be compared with other distal locking methods based on these criteria. Our device and software have some limitations and challenges that need to be addressed in the future, such as clinical validation, and regulatory approval.ConclusionThe device showed promising results in terms of low-cost, reusability, low radiation exposure, high accuracy, fast distal locking, high stiffness, and adaptability. The device has several advantages over other distal locking techniques, such as free-hand technique, mechanical aiming devices, electromagnetic navigation systems, and computer-assisted systems. We believe that our device and software have the potential to revolutionize the distal locking technique and to improve the outcomes and quality of life of the patients with long bone fractures.

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.

Ant Colony Optimization Based Edge Detection Algorithm

The problem of edge detection represents one of the most elementary assignments in image processing, providing an essentialbase for all further study and interpretation of the visual data analysis. This paper proposed an enhanced version of the Ant Colony Optimization (ACO) algorithm for edge detection. The following paper tries to compare the Proposed ACO method with the conventional techniques of edge detection like Canny, Prewitt, and Sobel, using various quantitative metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Entropy, Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) applied over different images. The datasets for this evaluation are considered as a standard Cameraman, a biological cell, and MRIimage, with and without noise, considering the ranges of complexities and textures.The results of our study prove the competencies of ACO's algorithm. In some cases, it stands out against standard algorithms for MSE and PSNR values and maintains high Entropy values, suggesting the robustness of detail-keeping in an image. Further, the quality assessment of the images by using NIQE and BRISQUE shows the ability of ACO to maintain a natural appearance post-edge detection. In this regard, the studyhighlights that the proposed ACO isan effective method for edge detection in varying image conditions,and, in doing so, it even validates the effectiveness of bio-inspired algorithms in image processing domains.

An efficient weapon detection system using NSGCU-DCNN classifier in surveillance

Weapon detection systems play a significant role in ensuring public safety, specifically in surveillance scenarios. But, poor performance has been shown by the prevailing research methodologies towards occluded and customized weapons. Thus, to resolve this issue, a Non-linear and Scalar Growing Cosine Unit based Deep Convolutional Neural Network (NSGCU-DCNN)-based efficient weapon detection system in the surveillance video is proposed. Primarily, the input video is converted into frames. The pre-processing process is applied for each frame. By utilizing Gaussian Kernelized Double Plateau Histogram Equalization (GKDPHE), contrast enhancement is performed grounded on the frame’s brightness. After that, for each frame, the motions are estimated by using the Frobenius Norm-based Three Step Search (FN-TSS) algorithm. Thereafter, by utilizing Weibull Distributed Viola Jones (WDVJ), the objects are detected, and the detected objects are cropped for further analysis. Next, the edge information of each object is determined by the Canny Edge Detection (CED) algorithm. Then, the frame is further converted into a 3x3 block. Afterward, the silhouette coefficient is calculated between the frames and the mask images. Further, the features are extracted and selected from the silhouette estimated frame. The NSGCU-DCNN classifies the weapons grounded on the selected features. The experimental outcomes exhibited that when contrasted with the prevailing techniques, the proposed mechanism withstands high-accuracy rates.

Edge detection in x-ray images of drill mast welds based on an improved Scharr operator

Rotary drilling rigs are extensively used in foundational construction, with main components including the mast, drill rods, drill bits, power head, and cockpit. The mast, a critical component, must be of high quality to ensure operational reliability. To ensure the reliability of the mast during operation, it is necessary to detect defects in the most stress-prone areas. Based on finite element analysis and practical engineering experience, the welds on the large circular disc of the drill mast are identified as the most vulnerable to damage. Accordingly, this paper prepared test specimens specifically from these weld locations and conducted X-ray inspections, obtaining images for analysis. Given the inevitable presence of noise due to objective conditions, the images first underwent median filtering. For effective subsequent defect detection, edge detection was performed on the images. Among various edge detection operators, the Scharr operator was found to be highly effective. To further enhance the edge detection performance, this paper introduces an improved Scharr edge detection algorithm that incorporates dynamic thresholds and Markov Random Field (MRF) techniques. The enhanced algorithm was evaluated using signal-to-noise ratio and edge localization accuracy as metrics, and it was found to significantly improve performance.

Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

Layer Contour Geometric Characterization in MEX/P through CIS-Based Adaptive Edge Detection

The industrial adoption of material extrusion of polymers (MEX/P) is hindered by the geometric quality of manufactured parts. Contact image sensors (CISs), commonly used in flatbed scanners, have been proposed as a suitable technology for layer-wise characterization of contour deviations, paving the way for the application of corrective measures. Nevertheless, despite the high resolution of CIS digital images, the accurate characterization of layer contours in MEX/P is affected by contrast patterns between the layer and the background. Conventional edge-recognition algorithms struggle to comprehensively characterize layer contours, thereby diminishing the reliability of deviation measurements. In this work, we introduce a novel approach to precisely locate contour points in the context of MEX/P based on evaluating the similarity between the grayscale pattern near a particular tentative contour point and a previously defined gradient reference pattern. Initially, contrast patterns corresponding to various contour orientations and layer-to-background distances are captured. Subsequently, contour points are identified and located in the images, with coordinate measuring machine (CMM) verification serving as a ground truth. This information is then utilized by an adaptive edge-detection algorithm (AEDA) designed to identify boundaries in manufactured layers. The proposed method has been evaluated on test targets produced through MEX/P. The results indicate that the average deviation of point position compared to that achievable with a CMM in a metrology laboratory ranges from 8.02 µm to 13.11 µm within the experimental limits. This is a substantial improvement in the reliability of contour reconstruction when compared to previous research, and it could be crucial for implementing routines for the automated detection and correction of geometric deviations in AM parts.

Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

A Study on the Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

High count rate alpha particle spectroscopy by a digital optical camera: A comparison study on edge detection algorithms

Alpha particle spectroscopy is highly significant in technologies related to radioactivity, particularly in radiation safety and the study of natural radioactivity. On the other hand, tools capable of identifying and determining the energies of alpha particles are of great interest. Among these tools, Commercial Off-The-Shelf (COTS) instruments, which can be used for alpha particle detection, have gained significant importance due to their ease of use and low cost. In this study, alpha particle spectroscopy of a Ra-226 source has been performed using a commercial digital camera based-on a CMOS sensor. In order to determine energy spectrum of alpha particles emitted from the source, the performance of various edge detection algorithms was examined. An algorithm based-on local pixel changes was introduced and implemented. By comparing the run times of the algorithms, it was shown that the algorithm proposed in this article exhibited the shortest run-time. Additionally, a method for rejecting pile-up events to correct their destructive effects on the alpha particle energy spectrum was introduced and implemented.

An Improved Algorithm of Image Edge Detection Based on the Degree Connection Situation

Aiming at the defect of the poor ability to suppress the noise of the algorithm of image edge detection, as well as too much sensitive to the noise, an improved algorithm of image edge detection based on SPA (set pair analysis) by combining trend relationship of the degree connection situation with the algorithm of half-neighborhood adaptive image edge pick-up is presented in this paper. The core of the algorithm is first to detect the most possible direction of the edge by using the biggest value of Degree Connection Situation--c/a, and then calculate absolute value of the D-value between the average value of three points and the average value of five points and the standard deviation value of all the points value in the 8-neighborhood, at last, decide whether the center-point is on the edge according to the threshold value of the standard deviation value. The result of simulation indicates that, without any processing to the image, the improved algorithm is not only accurate but also legible in object brim pick-up, compared with the algorithm in literature; furthermore, the noise is greatly suppressed by using it.

Метод виділення контурів для вирішення завдань моніторингу транспортної інфраструктури

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.