Canny Edge Detection Algorithm Research Articles

Object tracking method based on edge detection and morphology

With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes 3$$\\end{document} is improved to a convolution kernel of 2×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\ imes 2$$\\end{document} in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.

Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images

AbstractNon‐cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely intervention and appropriate management strategies. Extracting meaningful features from ultrasound (US) images requires robust and effective techniques. However, different feature extraction methods may yield varying results, and the choice of technique can influence the accuracy of fibroid detection. This paper presents an efficient approach for early detection of uterine fibroids in US images. Our proposed technique combines efficient feature extraction methods and a hybrid deep learning approach. Firstly, we employ the well‐known canny edge detection algorithm to accurately identify the edges of the fibroid region in the US images. This step helps in segmenting the target edge portion for further analysis. Additionally, we introduce an improved bird swarm optimization (IBSO) algorithm to extract a comprehensive set of features, utilizing both known features and newly obtained features. This approach enhances the accuracy of fibroid detection in uterine images. The proposed method uses a convolutional recurrent neural network (CRNN) to accurately detect and treat uterine fibroids, ensuring timely diagnosis and treatment. Our proposed IBSO‐CRNN model produces accuracy rates of 99.897% and 97.568% on the augmented and original datasets, respectively.

Optimization Scheme of Measuring Accuracy of Bearing Seat Size Based on Machine Vision

At present, the size measurement of industrial workpieces is mainly manual measurement and CMM measurement, which cannot meet the requirements of high precision, high efficiency and low error rate in industrial production at the same time. In order to solve the above problems, this paper uses monocular vision technology to propose an optimization scheme of workpiece size measurement accuracy based on machine vision, which takes the height and declination Angle between the camera lens and the workpiece as variables. Firstly, the image was captured by camera calibration correction, and morphological methods such as gray-scale, denoising, expansion and corrosion were used for image pre-processing. Secondly, features were extracted using double-threshold canny edge detection algorithm and connected domain algorithm. Finally, the bearing seat size was measured using the minimum external rectangle algorithm. The experiment shows that the higher the distance between the camera lens and the stage, the larger the error is when the image information can be collected completely. The greater the Angle between the center of the camera lens and the center of the workpiece, the greater the error; The device not only meets the precision requirements, but also has the advantages of fast response speed, which can meet the needs of industrial applications.

An oil and gas pipeline inspection UAV based on improved YOLOv7

This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.

MACHINE WHELL EDGE DETECTION MORPHOLOGICAL OPERATIONS

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning

The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.

Virtual registration of comminuted bone fracture and preoperative assessment of reconstructed bone model using the Procrustes algorithm based on CT dataset.

A research work was undergone in a virtual bone reduction process for reconstruction of the comminuted pelvic bone fracture using a CT scan dataset of patients. This includes segmentation, 3D model optimization and bone registration technique. The accuracy of the reconstructed bone model was validated using Finite Element Method. Analysed and applied various segmentation techniques to segregate the injured bone structure. The ICP (Iterative Closest Point), Procrustes algorithm and Canny edge detection algorithm were applied to understand the bone registration process for surgery in detail. The average RMS error, mean absolute distance, mean absolute deviation, and mean signed distance of the reconstructed bone model using proposed algorithms involving 10 patient datasets in a group were found to be 1.77, 1.48, 1.51 and -0.31 mm respectively. The calculated RMS error value proved minimal error in semi-automatic registration than other existing automatic registration techniques. Therefore, the proposed approach is suitable for virtual bone reduction for comminuted pelvic bone fracture. This method could also be implemented for various other bone fracture reconstruction requirements.

Machine vision-based automatic focusing method for robot laser welding system

Background Defocus distance is a critical parameter in laser welding, especially when encountering changes in the contour of the welding surface. This paper proposed an automated focusing method to address the challenging issue of accurately adjusting the defocus distance. Methods The proposed method involves several steps. Firstly, a clarity evaluation function based on the Kirsch operator is employed to calculate real-time image clarity of the welding surface captured by the machine vision system. Next, an improved Canny edge detection algorithm is applied to identify the edge contours of the welding surface, from which their central points are extracted. Finally, automatic focusing is achieved by employing a variable step-size hill-climbing algorithm to search for the focal plane. Results To verify the applicability of the automatic focusing method proposed for welding the solder ring, a robot laser welding system was designed and constructed. Experimental results show that the positioning error of the robot after automatic focusing is within ±0.4 mm. The average time required for a single automatic focusing process is 16.27 s. These results demonstrated the successful accomplishment of automatic adjustment and control of the focal length. Conclusions The machine vision-based automatic focusing method proposed in this paper enhances the consistency of the robot’s position after automatic focusing in robot laser welding systems. It elevates the level of automation in the welding process and provides an efficient solution for accurately adjusting the welding focal distance during the laser welding process.

A parallel Canny edge detection algorithm based on OpenCL acceleration.

In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.

A Review Based on the Comparison between Canny Edge Detection and Sobel Algorithm

A Review Based on the Comparison between Canny Edge Detection and Sobel Algorithm

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.

Smart monitoring of road pavement deformations from UAV images by using machine learning

Road pavement deformation monitoring is considered the main task for maintenance purposes, especially potholes and cracks, which are the most common types of road deformation surfaces. In order to make pavement inspections more effective, new types of remote sensing data that do not damage the pavement are being used more and more to find pavement distress. This article presents a proposed approach for extracting surface cracks from unmanned aerial vehicle (UAV) images using machine learning, focusing on the data pre-treatment processes. The objective of this study is to evaluate the effectiveness of decision tree classification (DT) in detecting cracks. The performance of the models is also evaluated. The performance evaluation approach is predicated on two primary criteria: model validation and testing. Also, the extent of the impact of post-classification operations, edge detection technology, and morphological processes on crack identification as well as classification accuracy, the digital orthomosaic was generated by the use of a technique commonly referred to as backward projection. To achieve this, the study uses a fusion of gray-level co-occurrence matrix (GLCM) attribute data and RGB images. Cracks are discovered using a classification tree (CT)-based classification approach with an overall classification rate of 86%. Ultimately, morphological processes using the closed image that was formed had a commendable level of accuracy, with an overall classification rate of 96%. The Canny edge detection algorithm has demonstrated its efficacy as a preferred method for detecting cracks from UAV images, providing invaluable decision support for actual road maintenance.

Recognizing surface edges and prediction of terrain deformation by analyzing LANDSAT-8 raster in QGIS- A case study of Coimbatore, India

Convolutional Neural Network (CNN) is one of the deep learning algorithms generally used for image recognition and allocation. These neural networks are developed in multi-layers which reasonably reduces complex dimensions of any input without ruining original information. The satellite images are obtained in .jpg format with potential resolutions. The land usage of the given area is estimated and the objects present in the image are identified using the Canny Edge detection algorithm. It extracts useful data in terms of structures and scales down the size of the data. Raster data in.tif format from LANDSAT-8 is collected over a year. With the Semi-automated classification plugin (SCP) in QGIS, the signatures are created. Signatures are pixelated polygons that are classified to store land attributes. The Normalized indices of vegetation (NDVI), water (NDWI), and built-up (NDBI) are calculated. Land use land cover area was developed. Multi-layer perceptron has numerous hidden layers, and the iterations can be fixed in the MOLUSCE plugin. The land cover for two years, 2020 and 2023 is given along with spatial variables such as precipitation and elevation. The changes in each category of land are identified. In the last three years, the area covered by buildings has increased from 25% to 31%. The area under water bodies had a slight decrease from 1.46% to 1.25%. The land cover for the year 2026 is predicted. From the predictions, it is conclusive that, our research supports the changes between 3 years had not much difference, but above 6 years, it is evident that land will be deformed from most of the vegetation area into built-up.

Performance Evaluation of Different Images Using Edge Detection Algorithms

To determine which edge detection method performs best and worst on different image types, numerous edge detection algorithms are examined. For the performance analysis, some sample photos from the web and some from Java are used as sources. The entropy and signal noise ratio are used to gauge how well the edged image performs. In image processing, conducting a thorough investigation of various edge detection techniques is highly worthwhile two widely used edge detection algorithms Log, and Canny—are taken into consideration in this analysis. Here in this paper, the analysis is focused on the performance of different edge detector algorithms. All candidate algorithms of edge detection are implemented in JAVA. The result of empirical performance shows that two variants namely canny perform better results for the edge detection algorithm. The result shows that when considering only the performance aspect. Cycle/byte is calculated for comparing different variants. Cycle/byte is decreased when the canny edge detector is examined. The canny edge detection algorithm shows a better performance than LoG. LoG has more than 3 times higher cycle/byte than Canny Edge detection.

A Method for Extracting Contours of Building Facade Hollowing Defects Using Polarization Thermal Images Based on Improved Canny Algorithm

During the service process of high-rise buildings, hollowing defects may be produced in the decorative layer, which not only affect the appearance, but also create a safety hazard of wall covering and shattered plaster peeling. Numerous studies have shown that hollowing can be detected using infrared thermal imagery under normal conditions. However, it is difficult to detect the edge and calculate the area of the hollowing on an exterior facade accurately because of the low contrast and fuzzy boundaries of the obtained infrared thermal images. To address these problems, a method for extracting the contours of building facade hollowing defects using polarization thermal images based on an improved Canny algorithm has been proposed in this paper. Firstly, the principle of thermal polarization imaging was introduced for hollowing detection. Secondly, considering the shortcomings of the Canny edge detection algorithm and the features of polarization thermal images, an improved Canny edge detection algorithm is proposed, including adaptive bilateral filtering to improve noise reduction ability while ensuring defect edges are not virtualized, Laplacian sharpening and histogram equalization to achieve contour sharpening and contrast enhancement, and eight-direction gradient templates for calculating image gradients, which make interpolation with non-maximum suppression more accurate, and the Tsallis entropy threshold segmentation algorithm based on the OTSU algorithm verification makes the image contour information more complete and accurate. Finally, a long-wave infrared polarization thermal imaging experimental platform was established and validation experiments were conducted. The experimental results demonstrate that the distinct, smooth, and precise location edges of the hollowing polarization infrared thermal images can be obtained, and the average error of the detected hollowing area is about 10% using the algorithm proposed in this paper.

Digital Image Processing On Kaffir Orange Peel With Canny Edge Detection Algorithm

Object tracking is a form of application of computer vision. To be able to track an object, a stage is needed in the image processing process. Image Processing is a field related to the process of image transformation (image). The image processing process is carried out to obtain better image quality. The harvesting system in kaffir lime is done manually, by choosing fruits whose skin color is green, and not yellowish. Due to the small and asymmetrical size and shape of kaffir lime, manual harvesting systems are still widely used to maintain the quality and quantity of the harvest. In addition, the manual harvesting system can also avoid damage to kaffir lime trees and obtain optimally ripe kaffir limes. Kaffir lime also has genders like humans, namely males and females. In male kaffir lime there is a circle that is more prominent in size underneath, while female kaffir lime has a flat shape. However, for consumption and medicinal purposes both male and female kaffir lime can be used without affecting the taste or quality of the fruit. With the image processing to determine the level of wrinkles on quality kaffir lime peel, kaffir lime will be selected which is usually used for herbal medicines. In this case, the Canny Edge Detection algorithm can be used to identify density edges in kaffir lime peels. Thus, the degree of wrinkles in kaffir lime peel can be calculated and measured to be more accurate. And can be separated quality or non-quality kaffir lime with the image of kaffir lime that has been seen through the image. The results obtained in designing and analyzing the quality of kaffir lime are clearer and more accurate with an image resolution value of 248 x 216 that the orange is included in the female kaffir lime type.The results tested that the right edge detection method in carrying out the edge detection process in the image of kaffir lime peel is the Canny Edge Detection Algorithm. By using the image on the Canny Edge Detection Algorithm, more dense and quality kaffir lime results are obtained so that it can be used for herbal medicine.

Enhancing Water Body Identification in Satellite Images through Comparative Analysis of Canny Edge Detection and Scale-Invariant Feature Transform Algorithms

Aim: The objective of this research is to utilize the Innovative Canny Edge Detection algorithm to precisely identify water bodies in satellite images acquired from the Water Access program. Subsequently, the performance of this approach will be evaluated by comparing it with the SIFT algorithm. The primary objective of this analysis is to contribute to the equilibrium of the water ecosystem. Materials and Methods: Within this study, two distinct groups were compared: the Innovative Canny Edge Detection algorithm (CED) (N=10) and the scale invariant feature transform algorithm (SIFT) (N=10). The total sample size was determined with the help of (g) power technology, considering an alpha of 0.05, an enrollment ratio of 1, a 93% confidence interval, and a power of 80%. Results: The Innovative Canny edge detection algorithm exhibited a noteworthy detection rate, achieving an accuracy of 85% compared to the SIFT algorithm's accuracy of 82%. The achieved results were devoid of errors, as indicated by the accuracy of the significance value (0.0001), substantiated by an 80% pre-test power in SPSS Statistical analysis. A significant statistical distinction between the two algorithms was observed. Conclusion: The Innovative Canny Edge Detection algorithm outperforms the SIFT algorithm with statistical significance.

Automatic left ventricle segmentation via edge‐shape feature‐based fully convolutional neural network

AbstractLeft ventricle (LV) segmentation is essential to identify the cardiac functions for treating cardiovascular disorders. Cardiovascular magnetic resonance (CMRI) imaging is a non‐invasive technique for diagnosing cardiovascular diseases. CMRI is widely used to assess the functional integrity of the left and right ventricles for detecting changes in myocardial structure. Clinical parameters of the LV are often retrieved from CMRI scans, such as LV volumes, and ejection fraction. Moreover, manually segmenting cardiac diseases and evaluating such functions is time‐consuming and difficult for medical professionals. Deep learning networks require a lot of time, cost, and knowledge. To overcome this issue, a novel Edge and Shape feature‐based Fully Convolutional Neural Network (ES‐FCN) has been proposed for automatic LV segmentation. The ES‐FCN model segments MRI images based on edge maps instead of using gray‐scale images, which accelerates the performance of the FCN. The fuzzy‐based canny edge detection algorithm leverages fuzzy logic to detect structural changes in the LV and generate binary images. Additionally, binary‐valued kernels are used for convolution operations, where the binary values are influenced by biases derived from edge map shape descriptors. In ES‐FCN, bias values are learned from ground truth segmentation. The proposed ES‐FCN model achieves the Jaccard indices of 0.9484 ± 0.0188 for the ACDC dataset and 0.9476 ± 0.0237 for the LVSC dataset, and the dice index of 0.9319 ± 0.0188 for the ACDC dataset and 0.9314 ± 0.0237 for LVSC dataset respectively. The experimental results also reveal that the proposed ES‐FCN model is faster and requires minimal resources compared to state‐of‐the‐art models.

Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots

To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while also reducing the computational memory required for subsequent image processing. Subsequently, an improved Canny edge detection algorithm is employed to extract the edges of foreground objects in the images. Additionally, a region-growing algorithm combined with the geometric features of the lines is employed to filter out unwanted thermal sources, enabling the accurate segmentation and extraction of power transmission lines. This forms a solid foundation for subsequent detection and identification of abnormal hotspots on the extracted lines, and holds significance for the inspection and maintenance of existing thermal faults and potential hotspots in power transmission lines.

Accurate Detection for Zirconium Sheet Surface Scratches Based on Visible Light Images.

Zirconium sheet has been widely used in various fields, e.g., chemistry and aerospace. The surface scratches on the zirconium sheets caused by complex processing environment have a negative impact on the performance, e.g., working life and fatigue fracture resistance. Therefore, it is necessary to detect the defect of zirconium sheets. However, it is difficult to detect such scratch images due to lots of scattered additive noise and complex interlaced structural texture. Hence, we propose a framework for adaptively detecting scratches on the surface images of zirconium sheets, including noise removing and texture suppressing. First, the noise removal algorithm, i.e., an optimized threshold function based on dual-tree complex wavelet transform, uses selected parameters to remove scattered and numerous noise. Second, the texture suppression algorithm, i.e., an optimized relative total variation enhancement model, employs selected parameters to suppress interlaced texture. Finally, by connecting disconnection based on two types of connection algorithms and replacing the Gaussian filter in the standard Canny edge detection algorithm with our proposed framework, we can more robustly detect the scratches. The experimental results show that the proposed framework is of higher accuracy.