Improved Canny Algorithm Research Articles

A Detection Method for the Edge of Cupping Spots based on Improved Canny Algorithm

A Detection Method for the Edge of Cupping Spots based on Improved Canny Algorithm

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.

A Method for Electric Vehicle Charging Port Recognition in Complex Environments based on Wavelet Enhancement and Improved Canny Algorithm

Background: The current method has low detection accuracy for electric vehicle charging ports in complex environments. background: The current method has low detection accuracy for electric vehicle charging ports in complex environments. Objective: This paper proposes a method for electric vehicle charging port recognition based on wavelet enhancement and an improved canny algorithm. objective: This paper proposes a method for electric vehicle charging port recognition based on wavelet enhancement and improved canny algorithm. Methods: Firstly, a preprocessing assessment model is proposed to determine whether preprocessing enhancement should be applied. Subsequently, we perform a stretch transformation on the S channel in the HSI color space and apply a discrete wavelet transform to the I channel to restore the brightness and detail features of the target objects in the image. Specifically, in this paper, adaptive median filtering and multi-scale Retinex algorithm are utilized on different frequency components respectively. Finally, the features and parameters of the charging port are detected and extracted through the maximum inter-class variance method, morphological processing, the improved Canny algorithm, and Hough transform fitting. Results: This paper conducts comparative experiments on preprocessing enhancement of charging port images in different environments and features detection of charging ports. The hole feature parameters detected by this method are consistent with the actual situation. Conclusion: The experiment proves that the method proposed in this paper can meet the requirements of precise identification of charging ports in complex environments.

Defects Detection of Lithium-Ion Battery Electrode Coatings Based on Background Reconstruction and Improved Canny Algorithm

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production.

Global vision object detection using an improved Gaussian Mixture model based on contour.

Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

Brake disc positioning and defect detection method based on improved Canny operator

AbstractFirstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection.

Optical Sensor-Based Image Texture Characterization for Image Simulation Applications in College Gymnastics Instruction

Abstract Gymnastics learning is characterized by imitation and reference, and a teaching model combining optical sensors and image simulation systems may have promising results in college gymnastics courses. Based on the introduction of the meaning of college gymnastics teaching, the article sorted out the value performance of the image teaching mode and also established an optical three-dimensional imaging system based on optical sensors for gymnastics movement to obtain gymnastic images. In the optical 3D imaging system, the acquisition error of gymnastics images is reduced by using the finite distance image measurement method, and the quality of gymnastics images is enhanced by adaptive bilateral filtering and adaptive threshold selection of the Canny operator by the OTSU method. The enhanced gymnastics images are then inputted into a panoramic graphic imaging simulation system, and a gymnastics image simulation teaching model is established for colleges and universities. The effectiveness of the gymnastics image simulation teaching model is analyzed through a teaching comparison test conducted during simulation verification for optical imaging. In the optical imaging system, when the measurement distance is about 150m, the measurement error range is between [15″and 30″], and the MSE value of the gymnastic enhanced image obtained by the improved Canny algorithm decreases by 46.97% compared with the MSE value before the improvement. The students’ handstand score against the wall improved by 22.63 points compared to before the experiment, and more than 90% of the students were satisfied with the gymnastics image simulation teaching method. The teaching mode is constructed with the optical sensor to obtain the image characteristics of gymnastics movement, and the simulation system is innovative, which effectively promotes students’ interest in learning gymnastics courses and enhances their gymnastics training level.

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.

A satellite component contour extraction method for lightweight space mobile platforms

PurposeOwing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms.Design/methodology/approachA contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction.FindingsExtensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes.Practical implicationsThe proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms.Originality/valueA pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

Edge detection algorithm in complex image text information extraction

With the rapid development of network technology and information technology, the amount of information contained in images has increased significantly. How to effectively extract text information from complex images has become the focus of current research in this field. Firstly, the Canny algorithm in the edge detection algorithm is improved and applied to the edge detection of complex images. Then the K-means algorithm is optimized to achieve better clustering effect of pixels. Finally, the text information in the image is extracted from the two. The results show that under the influence of salt and pepper noise from 0% to 90%, the quality factor obtained by the improved Canny algorithm is at least 0.4, and the detection accuracy is higher; The minimum peak signal-to-noise ratio of the algorithm is 38, and the maximum mean square error is 30, which are both better than the LOG algorithm and the traditional Canny algorithm, and have better noise reduction effect and image fidelity. It is used together in the extraction process of image text information, and the text recognition accuracy rate of the combined algorithm reaches a maximum of 93%, and is stable at more than 90%, indicating that this method has a high text recognition accuracy rate and provides text extraction for complex images. A reference path is available.

Design of An Intelligent Manipulator Controlled by Gestures Based on FPGA

With the development of science and technology, manipulators are increasingly used in industrial production and daily life. The manipulator with traditional control mode has high requirements for operators, complicated operation, and human-computer interaction is inconvenient. In this paper, an intelligent manipulator system based on gesture control is designed. FPGA is used as the control core of the system. We apply the improved Canny algorithm processing, which can automatically recognize gesture information. After judgment, the control signal is sent to the manipulator to control the manipulator to complete the specified action. Through experimental testing, the system can successfully recognize all kinds of gestures, and can well control the manipulator to complete all kinds of actions. The human-computer interaction is more real, which greatly improves the intelligence of the manipulator system and helps to improve the work efficiency.

FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm

Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations, and the division operation is complex and has low efficiency and slow speed. We introduce a logarithmic unit to turn the division into a subtraction operation that is easy to implement by hardware but does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 × 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms that have limited logical resources.

Object Edge Detection Algorithm Based on Improved Canny Algorithm

Object Edge Detection Algorithm Based on Improved Canny Algorithm

An Improved FAST Algorithm Based on Image Edges for Complex Environment.

In complex environments such as those with low textures or obvious brightness changes, point features extracted from a traditional FAST algorithm cannot perform well in pose estimation. Simultaneously, the number of point features extracted from FAST is too large, which increases the complexity of the build map. To solve these problems, we propose an L-FAST algorithm based on FAST, in order to reduce the number of extracted points and increase their quality. L-FAST pays more attention to the intersection of line elements in the image, which can be extracted directly from the related edge image. Hence, we improved the Canny edge extraction algorithm, including denoising, gradient calculation and adaptive threshold. These improvements aimed to enhance the sharpness of image edges and effectively extract the edges of strong light or dark areas in the images as brightness changed. Experiments on digital standard images showed that our improved Canny algorithm was smoother and more continuous for the edges extracted from images with brightness changes. Experiments on KITTI datasets showed that L-FAST extracted fewer point features and increased the robustness of SLAM.

An Optimized Digital Image Processing Algorithm for Digital Oil Painting

Human civilization’s accomplishments have grown with the passage of time and the advancement of society. With the fast growth of computer networks and information technology, the conventional method of information transmission based on words cannot fulfill the demands of people in the current era. As a result, in this age of extensive information and image processing techniques, images as a means of information sharing are becoming increasingly popular. As we know, digital image processing knowledge has a far-reaching impact in the field of artistic creation, among which the creation of oil painting is facing severe challenges. Aiming at the problem that the effect of digital oil painting is not ideal, this paper aims to study digital oil painting by using digital image processing technology. This paper first uses the image edge recognition based on the improved Canny algorithm to detect the edge of the oil painting image, then uses the nonlinear image enhancement algorithm to enhance the effect of the oil painting image, then uses the improved genetic algorithm to segment the image, and finally enlarges the oil painting image to calibrate the color of the oil painting image. Experiments reveal that the proposed approach outperforms existing algorithms in terms of edge detection data integrity, high-quality coefficient index of image enhancement, picture segmentation running time, and the ability to successfully increase the visual effect of oil painting.

Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm

The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional Canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved Canny algorithm was compared with the traditional Canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved Canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness.

Rapid Extraction of Clothing Sample Profile Based on the Improved Canny Algorithm

In order to reduce the difficulty of clothing diversity on the edge extraction link, this study proposes an improved Canny algorithm that segments the pattern, styles, and contours of the clothing image after creating a sample library of a women’s image. Extract, improve the accuracy of the edge, and reduce the noise formed by texture and clothing. The results show that in the simulation experiment, the contour extraction of multiple categories of clothing is carried out, and compared with the differential operator algorithm and Canny algorithm, the experimental results show that the improved algorithm can more accurately segment the edge of clothing, extract the style contour, and express the characteristics of clothing.

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury.

The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images. Firstly, the traditional Canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining Otsu algorithm and threshold iteration method. Then, the optimized algorithm was compared with the traditional Canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy. It was found that the peak signal-to-noise ratio (PSNR) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved Canny algorithm were better than the traditional Canny algorithm (15.2~28.61, 9.44~18.56). The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort (2.38 ± 0.15 points), dry humidity score (1.07 ± 0.21 points), antibiotic use (7.41 ± 0.74 days), and hospital stay (8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant (P < 0.05). In the experimental group, within 48 hours after extubation, the respiratory rate (RR), heart rate (HR), arterial partial pressure of carbon dioxide (PaCO2), and HCO3− were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (SpO2), mean arterial pressure (MAP), arterial partial pressure of oxygen (PaO2), and pH were significantly higher than the control group, and the difference was statistically significant (P < 0.05). In conclusion, the algorithm in this study is superior to the traditional Canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

Edge Location and Identification Method for Electronic Components based on Improved Canny Algorithm

The position identification of SMD electronic components mainly uses Canny edge detection algorithm to detect the edges of specific elements, benefited from its computational simplicity. The traditional Canny algorithm lacks the adaptability in gradient calculation and double thresholds selection, which may affect the location and identification accuracy of specific elements in electronic components. In this paper, an improved canny edge detection algorithm is proposed. The gradient magnitude is calculated in four directions, i.e., horizontal, vertical, and diagonal. Both the high and low thresholds can be adaptively determined based on the grayscale distribution information, to increase the adaptability of edge identification. The experimental results show that the proposed method can better locate the true edges of specific elements in electronic components with a reasonable processing speed, compared with the traditional Canny algorithm, and has been successfully applied on practical real-time vision inspection on SMD electronic components.

Comparison of edge discrimination of microbubbles between Sobel and improved mathematics of Canny edge detection

Bubble sizes are generated by micro-bubble generators (MBGs) in the water for their effect on the percentage of dissolved oxygen in the water and we find this in aquaculture where oxygen is important to marine life and in many applications. And since these bubbles range in size from 20 to 50, we need to highlight the shape of the bubble and distinguish it, so two Sobel algorithms were used and the Canny method was improved and compared between them, where the edge detection algorithm is sensitive to noise, and therefore, it is easy to lose weak edge information when filtering noise, and it appears ts fixed parameters are weak adaptability. In response to these problems, this paper proposed an improved algorithm based on the Canny algorithm. This algorithm introduced the concept of gravitational field strength to replace the image gradient and obtained the gravitational field strength factor. Two methods for choosing the adaptive threshold based on the average image gradation The size and standard deviation of two types of model images (one containing less edge information, the other containing rich edge information) were subtracted, respectively. The improved Canny algorithm is simple and easy to achieve. Experimental results show that the algorithm can retain more useful information and is more robust in the face of noise.