Improved Edge Detection Research Articles

Sliding window based adaptative fuzzy measure for edge detection

AbstractIn this work, we explore the impact of adaptive fuzzy measures on edge detection, aiming to enhance how computers interpret images by identifying edges more accurately. Traditional methods rely on analysing changes in image brightness to locate edges, but they often use fixed rules that do not account for the unique characteristics of each image. Our approach differs by adjusting fuzzy measures based on the information within specific areas of an image under a sliding window approach, utilizing a variety of fusion functions and generalizations of the Choquet integral to analyse and combine pixel data. The proposed method is flexible, allowing for the adaptation of measures in response to the image's local features. We put our method to the test against the well‐established Canny edge detector to evaluate its effectiveness. Our experimental results suggest that by adapting fuzzy measures for each image section, we can improve edge detection results.

Detection of Volcanic Ash Using Goes-16 Satellite Imagery: Case Study of Popocatépetl Volcano, Mexico

Objective: The objective of this study is to investigate the accuracy of volcanic ash detection using satellite imagery, to improve existing monitoring tools to adapt them to the Popocatépetl volcano in Mexico. Theoretical Framework: Volcanic ash detection via remote sensing employs Pavolonis' algorithms that compare radiances in the thermal and mid-infrared spectrum. This approach differentiates ash from clouds and water vapor by leveraging its differential absorption. Satellites such as GOES-16, which capture multispectral data, optimize the continuous detection and monitoring of ash dispersion, even in areas of low concentration. Method: Three volcanic ash detection algorithms were compared using images from the Popocatépetl eruption event on May 21, 2023: two developed by Pavolonis, which combine thermal and mid-infrared bands, and a third, a spectral adaptation designed to enhance precision at the edges of the ash cloud, tailored to the study area. Results were validated with an RGB ash image provided by NOAA. Results and Discussion: Pavolonis' algorithms provided a solid foundation for general detection, but the third algorithm, specifically designed for this study, significantly improved edge detection where classification conditions are more complex. This improvement was reflected in a higher agreement with the RGB ash image used as a reference. However, limitations related to atmospheric interference were identified, which require further adjustments in low ash concentration scenarios. Research Implications: The findings of this research have significant practical and theoretical implications for volcanic risk management and environmental protection. Enhanced accuracy in volcanic ash detection can optimize monitoring and early warning strategies, reducing risks to public health and infrastructure. Originality/Value: This study contributes to the literature by introducing a specific spectral adaptation to improve edge detection of volcanic ash, a novel approach not previously addressed. The research's relevance lies in its ability to refine monitoring methodology, providing more effective tools for volcanic risk management and offering a model applicable to other areas facing similar remote sensing challenges.

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.

Fractional differentiation based image enhancement for automatic detection of malignant melanoma

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Boundaries identification of geological structure in lunar Oceanus Procellarum region using full gravity gradient tensor methodology

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution.

SFMnet: Edge detection of HABs based on spatial feature mapping encoder-decoder network

Achieving high-accuracy real-time recognition of marine red tide algae images is crucial in implementing red tide algae early warning systems. However, due to uneven lighting and the deposition of unknown suspended particles in seawater, real-time collected harmful algal blooms (HABs) microscopic images in marine engineering suffer from poor clarity and blurred cell contours, leading to lower accuracy of current edge detection models for microscopic images. Firstly, to enhance the quality of degraded images in the original HABs dataset, we design an image enhancement algorithm based on underwater image restoration models, which involves precise depth estimation of seawater scenes and fusion of transmission maps. This algorithm effectively eliminates chromatic aberration and enhances image edge details and contrast. Secondly, we propose a deep-learning model based on the VGG16 network. This model integrates multi-scale features through the Spatial Feature Mapping module to improve edge detection accuracy. Experimental results using real-time HABs microscopic image data show that the proposed enhancement method achieves the best results regarding contrast, image enhancement metrics, and clarity gradient. Moreover, the edge detection method achieves an ODS of 0.636 and an OIS of 0.701. The results demonstrate that performing edge detection after the image enhancement preprocess significantly improves detection accuracy.

Field cabbage detection and positioning system based on improved YOLOv8n

BackgroundPesticide efficacy directly affects crop yield and quality, making targeted spraying a more environmentally friendly and effective method of pesticide application. Common targeted cabbage spraying methods often involve object detection networks. However, complex natural and lighting conditions pose challenges in the accurate detection and positioning of cabbage.ResultsIn this study, a cabbage detection algorithm based on the YOLOv8n neural network (YOLOv8-cabbage) combined with a positioning system constructed using a Realsense depth camera is proposed. Initially, four of the currently available high-performance object detection models were compared, and YOLOv8n was selected as the transfer learning model for field cabbage detection. Data augmentation and expansion methods were applied to extensively train the model, a large kernel convolution method was proposed to improve the bottleneck section, the Swin transformer module was combined with the convolutional neural network (CNN) to expand the perceptual field of feature extraction and improve edge detection effectiveness, and a nonlocal attention mechanism was added to enhance feature extraction. Ablation experiments were conducted on the same dataset under the same experimental conditions, and the improved model increased the mean average precision (mAP) from 88.8% to 93.9%. Subsequently, depth maps and colour maps were aligned pixelwise to obtain the three-dimensional coordinates of the cabbages via coordinate system conversion. The positioning error of the three-dimensional coordinate cabbage identification and positioning system was (11.2 mm, 10.225 mm, 25.3 mm), which meets the usage requirements.ConclusionsWe have achieved accurate cabbage positioning. The object detection system proposed here can detect cabbage in real time in complex field environments, providing technical support for targeted spraying applications and positioning.

SEFANet: Semantic enhanced with feature alignment network for semantic segmentation

To address the issues of poor segmentation accuracy and insensitivity to details in semantic segmentation, this paper proposes a novel image semantic segmentation framework SEFANet. Specifically, SEFANet adopts encoder-decoder structure, and incorporates a novel perceptual enhancement mechanism called Multi-scale Spatial Integration Module (MSIM) at the encoder. MSIM is based on group convolution to boost spatial semantic features and refine spatial-gradient semantic features within the multi-scale structure. This module enhances feature extraction across different network levels, leading to improved edge detection and segmentation abilities. In the decoder, SEFANet introduces a pixel-level Interleaved Feature Alignment Module (IFAM), which leverages rich semantic information in low-dimensional features and the strategy of Semantic Offset Field. Meanwhile, IFAM warps the high-dimensional feature map into low-dimensional features, completing the calibration process through convolution operations. Experimental results on the Pascal VOC2012 val dataset and the Cityscapes val dataset confirm the effectiveness and generalization of the proposed semantic segmentation. Additionally, the results further demonstrate that SEFANet improves the poor segmentation accuracy and insensitivity to details, and achieves a competitive performance compared with other semantic segmentation methods.

PiDiNet-TIR: An improved edge detection algorithm for weakly textured thermal infrared images based on PiDiNet

In order to solve the problems such as difficulties in extracting image edge information caused by low contrast and blurred edges of thermal infrared images, a thermal infrared image edge detection algorithm PiDiNet-TIR constructed based on the PiDiNet model is proposed. On the basis of the visible light image dataset BSDS500, the visible light image is grayed out, noise is added, and contrast is reduced to construct the thermal infrared image edge dataset I R-BSDS500, which provides image data for model training and testing. Deeply study the theory of PiDiNet model, add rich convolution mechanism to its backbone network, improve the activation function, pooling layer and up-sampling module, study the SimAM attention mechanism and add it to the side network. Using the open-source thermal infrared image dataset and homemade thermal infrared images for testing, we compare and analyze the mean square error (MSE), structural similarity of image (SSIM), feature similarity of image (FSIM), and Frames Per Second (FPS)of different detection algorithms, such as HED, RCF, DexiNet, PiDiNet, and the algorithm proposed in this paper. The results of image evaluation parameters prove that the edge detection algorithm proposed in this paper has high feature extraction efficiency and feature expression ability, and the algorithm detection performance is higher than other algorithms.

Machine vision recognition system for aerospace machined parts based on edge detection

Aerospace T-shaped machined parts are varied and have small structural differences. Manual identification has the problems of low efficiency and low accuracy. In order to realize efficient and accurate classification of aerospace machining parts, we built an image acquisition platform. To improve the edge detail extraction capability, we improved the edge detection algorithm based on deep learning. Furthermore, we employed the VisionTrain software to train recognition classification models for both large classes and subclasses. We then established a cross-granularity image classification process using VisionMaster software. Experimental results show that the improved edge detection algorithm in this paper is better than the existing common algorithm. The system achieves the goal of quickly and accurately recognizing all 60 machined parts.

Automated seismic semantic segmentation using attention U-Net

Seismic facies mapping from a 3D seismic cube is of significant value to various seismic interpretation and characterization tasks. Traditional facies mapping is based on examining sedimentary environments and stratigraphic sequences that provide distinct characteristics used for facies mapping. Given the complex nature of the task, manual facies mapping is typically time and labor consuming, and the quality of the decisions varies as a function of expertise. This complexity is further increased with the ever-increasing size of 3D seismic data sets. Deep-learning methods have indicated a promising potential to perform fast, accurate, and automated segmentation tasks. We investigate the application of machine-learning techniques, particularly state-of-the-art deep convolutional neural networks (CNNs), as a framework to perform accurate automated seismic facies pixel-wise segmentation. The workflow consists of a CNN-based U-Net architecture that adopts modern computer vision techniques. We develop three major changes to the standard U-Net to boost the performance for seismic semantic segmentation tasks: (1) using residual building blocks in the encoder, (2) using transformer-like attention gates after each residual block, and (3) using frequency spectrum data, in addition to seismic amplitude, as input to the network. We indicate that this implementation achieves higher accuracy metrics outperforming recently published state-of-the-art benchmarks. The performance of our method is validated using two 3D seismic data sets, the F3 Netherlands data set and the Penobscot data set acquired offshore Nova Scotia, Canada. Experimentation involves training on a set of samples and tuning the hyperparameters, followed by quantitative evaluation of the trained network. Our workflow produces high-quality segmentation with significantly reduced artifacts, improved edge detection, and improved lateral consistency throughout the seismic survey.

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image.

On Edge Detection Algorithms for Water-Repellent Images of Insulators Taking into Account Efficient Approaches

Computer vision has become an essential interdisciplinary field that aims to extract valuable information from digital images or videos. To develop novel concepts in this area, researchers have employed powerful tools from both pure and applied mathematics. Recently, the use of fractional differential equations has gained popularity in practical applications. Moreover, symmetry is a critical concept in digital image processing that can significantly improve edge detection. Investing in symmetry-based techniques, such as the Hough transform and Gabor filter, can enhance the accuracy and robustness of edge detection algorithms. Additionally, CNNs are incredibly useful in leveraging symmetry for image edge detection by identifying symmetrical patterns for improved accuracy. As a result, symmetry reveals promising applications in enhancing image analysis tasks and improving edge detection accuracy. This article focuses on one of the practical aspects of research in computer vision, namely, edge determination in image segmentation for water-repellent images of insulators. The article proposes two general structures for creating fractional masks, which are then calculated using the Atangana–Baleanu–Caputo fractional integral. Numerical simulations are utilized to showcase the performance and effectiveness of the suggested designs. The simulations’ outcomes reveal that the fractional masks proposed in the study exhibit superior accuracy and efficiency compared to various widely used masks documented in the literature. This is a significant achievement of this study, as it introduces new masks that have not been previously used in edge detection algorithms for water-repellent images of insulators. In addition, the computational cost of the suggested fractional masks is equivalent to that of traditional masks. The novel structures employed in this article can serve as suitable and efficient alternative masks for detecting image edges as opposed to the commonly used traditional kernels. Finally, this article sheds light on the potential of fractional differential equations in computer vision research and the benefits of developing new approaches to improve edge detection.

Geo‐information mapping improves Canny edge detection method

AbstractAiming at the shortcomings of the current Canny edge detection method in terms of noise removal, threshold setting, and edge recognition, this paper proposes a method for improving Canny edge detection by geo‐information mapping. The shortcomings of the traditional Canny edge detection method are analyzed by using the Canny optimal criterion and Tobler's First Law, which points out the direction of edge detection optimization by using the difference between edge properties and noise properties. The property characteristics and spatial distribution rules of edge points and edge lines are inspected using the geographic information mapping theory and technical methods, and edge identification criteria are defined at two levels of edge points and edge lines. Finally, the method model of improved Canny edge detection is constructed by combining guided filtering. The experimental results show that the improved edge detection method has the advantages of enriched edge details, accurate edge recognition, and strong self‐adaptive capability. This is a new attempt of geo‐information mapping theory and technical method in image edge detection, which has certain theoretical significance and strong practical guidance.

Managing deviant data in spatial frequency response (SFR) measurement by outlier rejection

The edge-based Spatial Frequency Response (e-SFR) method was first developed for evaluating camera image resolution and image sharpness. The method was described in the first version of the ISO 12233 standard. Since then, the method has been applied in a wide range of applications, including medical, security, archiving, and document processing. However, with this broad application, several of the assumptions of the method are no longer closely followed. This has led to several improvements aimed at broadening its application, for example for lenses with spatial distortion. We can think of the evaluation of image quality parameters as an estimation problem, based on the gathered data, often from digital images. In this paper, we address the mitigation of measurement error that is introduced when the analysis is applied to low-exposure (and therefore, noisy) applications and those with small analysis regions. We consider the origins of both bias and variation in the resulting SFR measurement and present practical ways to reduce them. We describe the screening of outlier edge-location values as a method for improved edge detection. This, in turn, is related to a reduction in negative bias in the resulting SFR.

A Least Significant Bit Steganographic Method Using Hough Transform Technique

Steganography is a data-hiding scientific branch that aims to hide secret data in an image, video, or audio. Image steganography methods try to embed a large amount of data into images with high imperceptibility. However, increasing the number of embedded data in the image decreases its quality. Therefore, in this paper, a new method based on Least Significant Bit (LSB) using Hough Transform is proposed to improve the stego image quality with increasing the amount of embedded data. The LSB is the common embedding steganography method due to its simplicity of implementation and low complexity. The proposed method inverts the LSBs of image pixels to enhance the quality of stego image. First, improved edge detection filter is used to detect edges areas. Then, we invert LSBs for the pixel in edge area pixels. Finally, the LSBs smooth area pixels of the cover image are inverted. The performance of the presented method is evaluated for the stego image quality and the amount of embedded data. The results show that the new method has better performance in comparison with the current methods in terms of Peak Signal-to-Noise Ratio (PSNR) and capacity.

Detection of microcracks in silicon solar cells using Otsu-Canny edge detection algorithm

The necessity for photovoltaic (PV) arrays has expanded due to the rising demand for solar electrical energy. The demand for solar cells has grown as they are a key component of the PV array. Recent years have seen a rise in solar cell production as a result of this demand. The quality of a silicon panel affects its lifespan and effectiveness in the production of solar power. The picture edge-detection method is regularly employed to identify silicon solar panel flaws. On the other hand, defect identification is impacted by the panel's grid shadow. The most modern defect detection technique uses artificial neural networks, and pre-training the model requires a large number of potential samples from picture data sets. This paper offers the innovative Otsu-Canny operator method as a fault discovery strategy to address this problem. Rapid and accurate identification of microcracks enhances the effectiveness of fault detection. The Otsu algorithm is used in this method to improve edge detection performance for microcracks by modifying the canny operator with a double threshold and eliminating the impact of grids on detection. The process was finished by performing noise removal with morphological operation, picture segmentation, and image binarization. The experimental findings demonstrate a significant improvement in purity and integrity of the image processing algorithm used in this research. The Otsu-Canny edge detection technique is more accurate than other conventional edge detection algorithms for detecting microcracks, with a 92.83 % detection rate.

In Situ Measurement Method Based on Edge Detection and Superpixel for Crystallization Imaging at High-Solid Concentrations

To facilitate measuring crystal sizes during batch crystallization at high-solid concentrations by using an invasive imaging system, an in situ imaging measurement strategy based on edge detection and superpixel is proposed for the ambiguous boundary problem of large amounts of crystals. Firstly, an image filtering is employed to cope with image degradation caused by noise disturbance and suspension turbulence in the crystallizer. Subsequently, an image segmentation method is developed by utilizing improved edge detection and superpixel, which can be easily performed for crystal extraction. Accordingly, crystal size measurement can be developed for evaluation of the crystal size distribution. The experiment results on α-form L-glutamic acid present the effectiveness of the proposed method.

IMPROVED EDGE DETECTION FOR SATELLITE IMAGES

Abstract. Edges are a key feature employed in various computer vision applications namely segmentation, object recognition, feature tracking and 3D reconstruction. Edges provide key information with regards to object presence, shape, form and detail which aid in many computer vision tasks. While there are various edge detection techniques in literature, challenges in edge detection remain. Varying image contrast due to non uniform scene illumination and imaging resolution affects the edge information obtained from any given image. The edge detection results are characterised by missing edges, edge fragmentation and some false positive edges. Gradient based edge detectors are the most commonly used detectors. These detectors all suffer from aforementioned challenges. In this, paper we present an edge detection framework that aims to recover long unfragmented edges from satellite images. This is achieved by using an edge accumulator that operates on the entire edge detection parameter space. Gradient based edge detectors rely on thresholding to retrieve salient edges. This usually results in missed or noisy edges. To counter this, the accumulator is run over a wide parameter space, growing edges at each accumulator level while maintaining edge position using a localisation filter. The results are longer unbroken edges that are detected for most objects, even in shadowy regions and low contrast areas. The results show improved edge detection that preserves the form and detail of objects when compared to current gradient based detectors.

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements.