Traditional Edge Detection Algorithms Research Articles

An Edge Detection Method for Welding Pool Based on An Improved Canny Algorithm

Abstract In this study, an improved Canny algorithm is proposed for detecting welding pool edges in complex environments, considering the issues of inaccurate extraction, discontinuity, and susceptibility to noise in traditional edge detection algorithms. Firstly, the original Gaussian filter is replaced with a hybrid filter that combines dark channel prior defogging and bilateral filtering used for image denoising. Then, a four-direction Sobel operator is utilized to calculate gradient amplitude and direction. Furthermore, the double threshold required for extracting and connecting pool edges is obtained by using an enhanced quadratic Otsu algorithm. Experimental results demonstrate that, compared with the traditional algorithm, the improved Canny algorithm achieves an approximately 51.6% increase in PSNR and about 0.04 improvement in SSIM, enabling more accurate and comprehensive extraction of welding pool edge information.

Improved 3D human face reconstruction from 2D images using blended hard edges

This study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination.

ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection

Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.

EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION

The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed signboards, scratches on traffic symbols, discontinuous or damaged traffic symbols, etc. These challenging conditions are presented for various reasons, such as accidents, storms, artificial damage, etc. Such traffic signs contain an ample amount of noise, because of which traffic sign identification and recognition become a challenging task for automated TSIRS systems. The proposed method in this paper addresses these challenges. The sign edge is a helpful feature for the recognition of traffic signs. A novel traffic sign edge detection algorithm is introduced based on bilateral filtering with adaptive thresholding and varying aperture size that effectively detects the edges from such noisy images. The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.

Using Convolutional Neural Networks for Edge Detection in Medical Images to Determine Surgery Instrument Tools

Edge detection plays a crucial role in medical image analysis, particularly in surgical settings where accurate identification of surgical instrument tools is essential. In this paper, we explore the use of Convolutional Neural Networks (CNNs) for edge detection in medical images to determine surgical instrument tools. We present a comprehensive study that includes dataset selection, preprocessing techniques, network architecture design, training procedures, evaluation metrics, and experimental results. The CNN models were trained on a diverse dataset of medical images with annotated ground truth edge maps. The models demonstrated superior performance compared to traditional edge detection algorithms and handcrafted feature-based approaches, achieving high accuracy and robustness in capturing surgical instrument boundaries. We evaluated the models using metrics such as Intersection over Union (IoU), Precision, Recall, F1-Score, and Mean Average Precision (mAP) on a separate test set. this study demonstrates the potential of CNNs for edge detection in medical images to determine surgical instrument tools. The achieved accuracy, robustness, and computational efficiency of the trained models validate their utility in assisting surgeons during surgical interventions.

Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED

Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found.

Evaluation of edge detection algorithm of frontal image of facial contour in plastic surgery

With the improvement of medical levels and the continuous improvement of people’s living standards, the demand for beauty by the general public is increasing. The plastic surgery industry has also developed by leaps and bounds. People’s dissatisfaction with their own facial appearance, facial injuries and some other reasons have prompted people to carry out facial reconstruction, and facial plastic surgery has developed rapidly. However, in the current facial plastic surgery, the edge detection effect on the contour image is general. In order to improve the edge detection effect of facial contour lines in medical images, this paper proposed a facial contour line generation algorithm. First, the detection effects of four operators were compared. After comparing the effects, the Sobel operator was used as the input data to generate an edge detection algorithm. Then, the grayscale features of the tissue in the image and the symmetry of the image were used to perform bidirectional contour tracking on the detected image to extract facial contour lines. In addition, for facial contour features, the midpoint method can be used to generate auxiliary contours. The algorithm was verified by a set of facial CT (Computed Tomography) images in the experiment. The results showed that the new generation algorithm accelerated the edge detection speed, had good denoising performance, and enhanced the edge detection effect by about 12.05% compared with the traditional edge detection algorithm. The validity and practicability of facial edge detection were verified, and it provided a theoretical basis for further realizing the design of a facial contour digital image processing system.

Edge enhancement improves adversarial robustness in image classification

Imperceptible adversarial examples are capable of deceiving the deep neural networks with high confidence. Recent studies show that it is particularly effective to control the attack space to low-frequency components of the image on the basis of adversarial training. However, those methods face the problem of losing valuable knowledge, especially shape information, which is vital for classification and robustness. To alleviate this issue, we propose a new method based on edge detection, named Edge Enhancement (EE), which can explicitly make up for the missing shape information in frequency constraints and further enhance the adversarial robustness. Specifically, we first employ a traditional edge detection algorithm called Canny to obtain shape information due to its simplicity and intrinsic robustness. Then, we augment the low-frequency space via obtained shape features, with the weighting operation carried on. This operation can be regarded as an emphasis on shape information, which could mitigate the texture bias of deep neural networks, thereby further serving the robustness. Finally, we feed the augmented features into the deep neural network. It is worth noting that these modules are optimized along with the deep neural network, which enables an end-to-end training fashion. Experimental results show that our proposed model can significantly improve adversarial robustness over the state-of-the-art methods on three benchmark datasets, including MNIST, Tiny ImageNet, and particularly ImageNet. For example, our method achieves 51.66% accuracy on ImageNet under 10-iteration targeted PGD white-box attack where the prior art has 36.94% accuracy. Code is available at https://github.com/Aiqz/Edge-Enhancement.

Survey of Image Edge Detection

Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. This technology has been widely used in image segmentation, target detection, and other high-level image processing technologies. In recent years, considering the problems of thick image edge contour, inaccurate positioning, and poor detection accuracy, researchers have proposed a variety of edge detection algorithms based on deep learning, such as multi-scale feature fusion, codec, network reconstruction, and so on. This paper dedicates to making a comprehensive analysis and special research on the edge detection algorithms. Firstly, by classifying the multi-level structure of traditional edge detection algorithms, the theory and method of each algorithm are introduced. Secondly, through focusing on the edge detection algorithm based on deep learning, the technical difficulties, advantages of methods, and backbone network selection of each algorithm are analysed. Then, through the experiments on the BSDS500 and NYUD dataset, the performance of each algorithm is further evaluated. It can be seen that the performance of the current edge detection algorithms is close to or even beyond the human visual level. At present, there are a few comprehensive review articles on image edge detection. This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of edge detection and to make further improvements and innovations.

Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

Brain Tumors are the leading cause of cancer death in children. They are caused by the abnormal and uncontrolled growth of cells inside the brain or spinal canal. Classification of brain tumors using machine learning technology is very relevant for radiologists to confirm their analysis more effectively and quickly. Segmentation algorithm identified for detecting the tumor from the MRI brain scans need to detect shapeless tumor growth perfectly. Sobel edge detection is one of the widely used edge detection techniques in which only information along horizontal and vertical directions are considered. In this research, Sobel algorithm with 8-directional template is implemented for improving the detection of edges in brain tumor MRI images. The proposed algorithm is compared with other traditional edge detection algorithms. The performance of the proposed algorithm is analyzed in terms of MSE, RMSE, Entropy, SNR and PSNR. Analysis shows that 8-Sobel is comparatively the most suitable technique for analyzing brain tumor MRI images. Active contouring segmentation algorithm is applied on the edge detected images to verify the classification accuracy of segmented tumor.

SSWS: An edge detection algorithm with strong semantics and high detectability for spacecraft

Space tasks such as docking, capture and maintenance of spacecraft in orbit are increasingly dependent on visual system. High-order features extraction, target recognition and spacecraft pose measurement are essential image processing technologies to accomplish above tasks, for which image edge detection is one of the most critical and basic techniques. Traditional edge detection algorithms such as Canny have good extraction effect, but cannot extract and obtain semantic information. Besides, most of the edge detection algorithms based on deep-learning rely on a large number of annotation labels and require huge workload. Therefore, they cannot be applied to non-cooperative space targets. In this paper, we propose an edge detection algorithm with strong semantics and weak supervision (SSWS) to combine edge detection with image semantic segmentation. By learning geometric boundaries of ellipse and quadrilateral for docking ring and solar panel of spacecraft, and using the training method of weak supervision, our algorithm achieves effective boundary detection of important parts of spacecraft from images regardless of the background interference and light intensity. Real image experiments show that SSWS can effectively extract the boundary of important parts of spacecraft and filter out the background information and noise. In the performance verification experiment of BSDS500 dataset, with weakly supervised annotation tags, SSWS obtains the best OIS (0.831), in comparison with traditional edge detection algorithms and excellent recent deep-learning edge detection algorithms in recent years; in the contrast experiment of image segmentation in PASCAL VOC 2012 test set, SSWS gets the best mIoU value (66.9%).

Edge detection algorithm based on valley lines and watershed

The purpose of edge detection algorithm is to highlight the edge in the image, which is also the premise and key of image segmentation. However, the traditional edge detection algorithm will amplify the influence of noise in the process of derivative operation, and the noisy edges will often appear, which is not conducive to the accurate extraction of edge. To solve this problem, we propose an edge detection algorithm combining valley lines and watershed algorithm. In this algorithm, the bilateral filter is used to replace the Gaussian filter to achieve edge preserving and denoising. The rewritten Canny operator is used to calculate the low value points and get the valley lines of the image. This process can achieve the first denoising. Then, the valley lines are used to replace the discrete points of water injection and the watershed algorithm is used to calculate the segmentation results of the image. At this time, there are many pseudo edges around the main boundary, and the pseudo edges are deleted by setting the threshold, so as to achieve the effect of secondary denoising. The experimental results show that our method has good performance in noise resistance and edge continuity compared with other and traditional edge detection algorithms.

Advanced Color Edge Detection Using Clifford Algebra in Satellite Images

Edge detection is widely used for image processing to improve the detection and classification of objects, segmentation, and extraction of other features. Satellite images are rich in information about objects with different color intensity and have a large amount of noise, so it is difficult to achieve recognition, classification, and feature extraction of small objects through traditional edge detection algorithms. The colors in satellite images suffer from a large amount of overlap due to areas or weather conditions that generate a lot of noise. Edge detection provides detailed information about objects in an image by reducing unnecessary feature information. Edge detection in color images is more challenging than edge detection in gray-level images. This paper proposes a method for the edge detection of color images using Clifford algebra and its sub-algebra, quaternions. Quaternion-based Fourier transform is used to process red, green and blue (RGB) images separately in the vector field. A 3×3 quaternion mask is developed to filter out frequencies of the image in multiple directions and only provides details about the edges. The algorithm works on three channels individually; the output is then processed through quaternion Fourier transform (QFT) and inverse QFT with a 3×3 mask to filter high frequencies. The proposed algorithm is compared with traditional edge detection algorithms using a satellite image dataset that has different types of objects and detailed information. Results are validated through entropy, structure similarity, and noise error to prove that our proposed algorithm provides satisfactory performance on different remote sensed images.

TIG Stainless Steel Molten Pool Contour Detection and Weld Width Prediction Based on Res-Seg

As the basic visual morphological characteristics of molten pool, contour extraction plays an important role in on-line monitoring of welding quality. The limitations of traditional edge detection algorithms make deep learning play a more important role in the task of target segmentation. In this paper, a molten pool visual sensing system in a tungsten inert gas welding (TIG) process environment is established and the corresponding molten pool image data set is made. Based on a residual network, a multi-scale feature fusion semantic segmentation network Res-Seg is designed. In order to further improve the generalization ability of the network model, this paper uses deep convolutional generative adversarial networks (DCGAN) to supplement the molten pool data set, then performs color and morphological data enhancement before network training. By comparing with other traditional edge detection algorithms and semantic segmentation network, it is verified that the scheme has high accuracy and robustness in the actual welding environment. Moreover, a back propagation (BP) neural network is used to predict the weld width, and a fitting test is carried out for the pixel width of the molten pool and its corresponding actual weld width. The average testing error is less than 0.2 mm, which meets the welding accuracy requirements.

An APF-ACO algorithm for automatic defect detection on vehicle paint

As a popular technology in the field of artificial intelligence, computer vision is gradually adapting to the needs of convenience for human beings, improving production efficiency and reducing production costs. Therefore, this study proposes a computer vision algorithm to locate and identify the location of defects. For the traditional edge detection algorithm Sobel, LoG, Canny, the decisive factor for the detection effect of paint defect image is the adjustment of parameters, which can’t achieve an adaptive edge detection algorithm for paint defects, so it is thought that the evolution idea of ant colony algorithm can be used to achieve accurate detection of defects. This paper proposes an automatic detection method for vehicle body paint film defects based on computer vision. An ant colony optimization edge detection algorithm based on automotive paint features (APF-ACO) is proposed. By combining global update and local update, the convergence speed of ant colony algorithm is improved and a new pheromone calculation and update method is proposed to effectively preserve the edge details of the detected image. A reflection area detection algorithm based on HSV color space is designed to detect the reflective area and eliminate interference. Establish defect classification identification rules, identify and mark five types of defects, and determine defect categories. Experiments show that the method can effectively detect the defect area and the recognition accuracy is 97.76%.

Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

A Pre-processing Step for Efficient Edge Extraction

In this paper, we propose a pre-processing step for an efficient edge extraction technique that takes input as an original image to generate an edge map. Generated edge maps could be inputted for state-of-art traditional edge detection algorithms like Canny, Sobel, Prewitt, and recent edge detection algorithms gb-UCM, CED Contours, Structured forest, Sparse Code Gradients and CNN based edge detection Deep Edge, N4 to get better performance. Further, the proposed algorithm has not required any training or learning to improve the edge detection method and is not depending on any parameters. Visual experiments and quantitative evaluation results show that our proposed algorithm greatly improves the modal quality of edge/edge maps. It preserves the original shape, structure of the objects and local features, which presents in an input image. The proposed method takes very less amount of time to execute and making it more suitable for real-time image processing and computer vision applications that depend on edge like classification, object localization, object recognition, image retrieval, segmentation, shape representation.

Structural crack detection using deep learning–based fully convolutional networks

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

Edge detection for optical synthetic apertures based on conditional generative adversarial networks.

Detecting the interference fringes of the optical synthetic aperture is the core in preventing misalignments of the sub-mirrors in piston, tip, and tilt. These fringes are characterized as follows: (1)the edge information of sub-mirrors is accompanied by complex shapes and large gaps; and (2)the traditional edge detection algorithms have different optimal thresholds under different interference fringes, and they may lose boundary information. To address these problems, a novel method for detecting the edge of synthetic aperture fringe images is proposed. Because conditional generative adversarial networks avoid the difficulty of designing the loss function for specific tasks, they are suitable for our project. We trained over 8000 images based on real images and simulated images. Experiments prove that the proposed method can reduce the false detection rate to 0.2, compared with 0.56 by Canny algorithm. This method can also directly detect the fringe edge of the optical synthetic aperture systems, which are accompanied by varied shapes and a growing number of sub-mirrors. When the input images lose boundary information, the traditional algorithm does not restore the boundary, but the proposed method makes a decision globally, and thus it guesses and then fills the boundary. The maximum error of the generated boundary and the actual boundary is two pixels.

Road crack edge detection based on wavelet transform

In this paper, the edge detection of road surface crack images is studied. The traditional edge detection algorithms such as Gaussian-Laplacian and Canny operator are used to extract the edge of the crack. The experimental results show that the edge extraction is not complete and sensitive to noise. Therefore, the modern wavelet analysis method is adopted to extract the complete and accurate crack edges.