Edge Detection Model Research Articles

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

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

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.

Application of image processing technology based on field programmable gate array in mechanical part inspection

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.

Neuromorphic computing spiking neural network edge detection model for content based image retrieval

ABSTRACT In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.

Adaptive weighted total variation expansion and Gaussian curvature guided low-dose CT image denoising network

The denoising task of low-dose CT images is a highly complex and uncertain inverse problem. Previous studies have primarily relied on convolutional neural network to reduce noise by learning the mapping from LDCT images to normal dose CT images. However, simply increasing the network depth alone is not an optimal choice due to the limited performance improvement and significant computational cost. In contrast, integrating prior knowledge of images with a model to assist in image reconstruction is a more efficient approach. This study proposes a new framework for denoising LDCT images, named Noise-Optimized Edge Feature Guided Network (NEFGN). The task of NEFGN is to integrate the noise optimization model of adaptive weighted total variation expansion, the edge detection model guided by Gaussian curvature, and image reconstruction into an end-to-end CNN framework. In order to achieve this goal, the noise optimization model is first constructed by learning the parameters in the adaptive weighted total variation regularization model to approximate the noise level in the NDCT image. The edge detection network is constructed using Gaussian curvature, predicting clear edges directly from the noise image. Finally, under the guidance of the noise optimization model and the edge detail model, NEFGN is more capable of suppressing artifact noise, demonstrating good accuracy and robustness, and can restore finer details. Numerous experimental studies demonstrate that the NEFGN denoising framework effectively restores the structure of LDCT images with limited image details and outperforms other methods in terms of performance.

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network.

Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

Refined edge detection model based on RCF

Refined edge detection model based on RCF

A Lightweight Pipeline Edge Detection Model Based on Heterogeneous Knowledge Distillation

A Lightweight Pipeline Edge Detection Model Based on Heterogeneous Knowledge Distillation

Automatic extraction of flat sketch design element from clothing images using artificial intelligence

This study aims to develop a process that automatically extracts various flat sketch elements in vector format from clothing images. The approach of this study is to combine state-of-the-art image processing algorithms with the new algorithm devised in this study. First, a convolutional neural network based edge detection model trained with specially prepared fashion image data set was used to convert clothing images into edge maps. Then, a recurrent neural network based vectorization model and a rule-based image correction algorithm were used to convert the edge map into a vector image. Finally, a graph search algorithm was used to extract closed shapes from the vector image. As a result, the accuracy of edge extraction model has been improved by training with the special fashion image data set. The image correction rule was able to refine the vector images generated by the vectorization model. The graph algorithm was able to extract closed shapes from the vector image. This study is the first study to extract vector style flat sketch elements from clothing images using artificial intelligence and conventional computational geometry.

Improved droplet edge detection model based on RCF algorithm

Improved droplet edge detection model based on RCF algorithm

Enhanced edge detection model for low resolution images

The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes.

Coronal Loop Detection Using Multiscale Convolutional Neural Networks

Solar magnetic fields play an important role in many solar activities, such as the solar wind, coronal mass ejections, and coronal oscillation. Coronal loops are curvilinear structures in the solar atmosphere and are closely related to coronal magnetic fields, so the study of their structure is very important. However, it is difficult to identify coronal loops accurately because of the complexity of their features. Therefore, we propose a two-stage detection method, using multiscale convolutional neural networks, to identify coronal loops. The regions including initial coronal loops are first marked by a improved Res-UNet model. The loop structures in the region are then detected using a improved dense extreme inception network for edge detection model. We selected the coronal images observed by the Transition and Coronal Explorer and the Atmospheric Imaging Assembly of the Solar Dynamics Observatory in the 171 Å channel to illustrate the detection processing. Meanwhile, we also compared the accuracy of our method to others. The results demonstrate that our proposed method has a high recognition rate and good robustness over previous identification methods and can be used to study the physical characteristics of coronal loops.

MultiResEdge: A deep learning-based edge detection approach

Edge detection is a fundamental technique in image processing and computer vision that plays a crucial role in various applications including object recognition, shape analysis, segmentation, feature extraction, image enhancement, image understanding, compression, and preprocessing. It involves identifying the boundaries or edges between different objects or regions which enables us to extract valuable visual information, analyze image structure, and facilitate subsequent computer vision tasks. In this study, we highlighted two primary issues that are faced in edge detection: edge connectivity and edge thickness. Choosing the right threshold for reliable edge detection has long been one of the most difficult problems in traditional edge detection techniques. To deal with these issues, the present research is motivated to execute a new deep learning-based edge detection model named MultiResEdge which is a tweaked variant of the original UNet framework. Instead of directly integrating encoder and decoder features, the suggested model makes use of a MutiRes block to modify spatial information from different scales, and a residual path with a set of convolutions to convey the features from the encoder to the decoder. To reduce the gap between the encoder and decoder features, we advocate using a combination of convolutional and skip connections. We also incorporated semantic edge information with semantic curves to improve the border. In addition, we have integrated a multi-step preprocessing technique that mitigates the false positives and false negatives and hence improves the accuracy, precision, recall, and F1 score. The extensive experiment is performed using images from two benchmark datasets to justify the predicted edge comparing it with the corresponding ground truth in terms of various objective metrics (such as entropy, MSE, PSNR, SSIM, and FSIM). Overall, our proposed edge detection model has obtained an accuracy of 99% along with an F1 score of 98% which is better than the related state-of-the-art methodologies.

The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network

The first step in petrographic image analysis is to segment the mineral grains in the thin section petrographic image, allowing the petrographer to identify the rock based on the grain size and composition. The purpose of this research is to present an automated computer vision solution for obtaining accurate and efficient edge segmentation maps. The proposed model, termed the extinction consistency perception network, is made up of three sections. The multi-angle extinction consistency block, which is based on the extinction consistency of mineral grains, uses consecutive petrographic images to generate edge-enhanced features. Then they are processed by the multi-scale edge perception network to obtain rich expression of edge features at different levels. Afterward, a distance-map penalized compound loss function is introduced to guide the model to pay more attention to grains’ edges. The generated cross-polarized petrographic image dataset (CPPID) with meticulous annotations has been shared with the community. Experimental findings show that the proposed model is effective, which is evaluated on CPPID and scores 0.940 ODS and 0.941 OIS, outperforming seven classic edge detection models by a large margin.

Learning parallel and hierarchical mechanisms for edge detection.

Edge detection is one of the fundamental components of advanced computer vision tasks, and it is essential to preserve computational resources while ensuring a certain level of performance. In this paper, we propose a lightweight edge detection network called the Parallel and Hierarchical Network (PHNet), which draws inspiration from the parallel processing and hierarchical processing mechanisms of visual information in the visual cortex neurons and is implemented via a convolutional neural network (CNN). Specifically, we designed an encoding network with parallel and hierarchical processing based on the visual information transmission pathway of the "retina-LGN-V1" and meticulously modeled the receptive fields of the cells involved in the pathway. Empirical evaluation demonstrates that, despite a minimal parameter count of only 0.2 M, the proposed model achieves a remarkable ODS score of 0.781 on the BSDS500 dataset and ODS score of 0.863 on the MBDD dataset. These results underscore the efficacy of the proposed network in attaining superior edge detection performance at a low computational cost. Moreover, we believe that this study, which combines computational vision and biological vision, can provide new insights into edge detection model research.

Research on Edge Detection Model of Insulators and Defects Based on Improved YOLOv4-tiny

Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high detection speed. First, in the neck network, the inverted residual module is introduced to perform feature fusion to improve the positioning ability of the insulators. Then, a high-resolution detection output head is added to the original model to enhance its ability to detect defects. Finally, the prediction boxes are post-processed to incorporate split object boxes for large-scale insulators. In an experimental evaluation, the proposed model achieved an mAP of 96.22% with a detection speed of 10.398 frames per second (FPS) on an edge computing device, which basically meets the requirements of insulator and defect detection scenarios in edge computing devices.

Seismic interpolation via multi-scale HU-Net

The recovery of the seismic data with missing traces is essential for seismic interpretation and reservoir characterization. The deep learning (DL) based models have shown the promising results for the challenging task of seismic data interpolation. However, interpolating the large gap missing traces is a big challenge for seismic interpolation. The holistically-nested edge detection (HED) model can learn the multi-scale and multi-level features for the holistic image training and prediction. The U-Net model has a large number of feature channels in the upsampling part, which allow it propagate the feature maps to the higher resolution layers. Utilizing the advantages of these two state-of-art DL models, we propose a novel convolution neural network (CNN) based model for seismic interpolation, named the HU-Net model, by integrating the holistically-nested module and the U-net model. To formalize this, we add five activation layers followed by the U-Net model to replace the origin sigmoid layer. We then use five side branches to connect the decoder with these activation layers and output multi-scale features. Thus, our proposed HU-Net cannot only recognize the objects in big images, but also reconstruct the multi-scale features. We investigate the ability of our proposed HU-Net for interpolating the missing seismic traces with big gaps and reconstruct the fining sequence texture simultaneously. The synthetic and field data examples illustrate the effectiveness and robustness of our proposed HU-Net model. The detailed comparisons also demonstrate the superiority of our proposed model over the conventional methods.

Multiscale hierarchical attention fusion network for edge detection

Edge detection is one of the basic challenges in the field of computer vision. The results of most recent methods produce thick edges and background interference. The images generated by these networks must be postprocessed with non-maximum suppression (NMS). To tackle the problem, we propose a novel edge detection model that allows the network to concentrate on learning the contextual features of an image, thereby obtaining more accurate pixel edges. To obtain abundant multi-granularity features of image high-level features, we introduce multi-scale feature stratification module (MFM). Then, we increase the constraint between pixels through the edge attention module (EAM), so that the model can obtain stronger feature extraction ability. These new approaches can improve the ability of describing edges of models. Evaluating our method on two popular benchmark datasets, the edge image predicted by this method is superior to existing edge detection methods in subjective perception and objective evaluation indexes.

Gauss Gradient Algorithm for Edge Detection in Retinal Optical Coherence Tomography Images

Medical imaging modalities play a vital role in the healthcare sector for disease diagnosis and prediction. Optical Coherence Tomography uses near-infrared rays for generating cross-sectional images of the retina. Edge detection is a traditionalROI extractionalgorithm thatextracts the boundary of objects in an image. This research work focuses on the gauss gradient-based edge detection model for boundary detection in Optical Coherence Tomography images of the retina. The separable feature of a 2D Gaussian kernel is used, and a 1D kernel for the x and y directions is created. The Gaussian kernel utilized in this research work is the convolution result of Gaussian function and first-order derivative of Gaussian function. For performance validation, the Berkeley segmentation data set was utilized, when compared to traditional edge detection models, and better results were obtained for the gauss gradient algorithm.

Pixel Difference Unmixing Feature Networks for Edge Detection

The edge detection model based on deep learning significantly improves performance, but its generally high model complexity requires a large pretrained Convolutional Neural Networks (CNNs) backbone, and hence large memory and computing power. To solve this problem, we carefully choose proper components for edge detection, introduce a Multiscale Aware Fusion Module based on self-attention and a feature-unmixing loss function, and propose a lightweight network model, Pixel Difference Unmixing Feature Networks (PDUF). The backbone network of proposed model is designed to adopt skip long-short residual connection and does not use pre-trained weights, and requires straightforward hyper-parameter settings. Extensive experiments on the BSDS, NYUD, and Multi-cue datasets, we found that the proposed model has higher F-scores than current state-of-the-art lightweight models (those with fewer than 1 million parameters) on BSDS500 (ODS F-score of 0.818), NYUDv2 depth datasets (ODS F-score of 0.767) and Multi-Cue dataset (ODS F-score 0.871(0.002)), with similar performance compared with some large models (with about 35 million parameters).