Gray Value Differences Research Articles

Sample Size Comparison in Area-Based Image Matching: Cross-Correlation and Mean Relative Gray Value Difference

Abstract Geospatial information is data about a specific location with geographic coordinates that can be collected, modified, and displayed in real-time. Geospatial data can be collected through field surveys and aerial photogrammetry. Aerial photos and Digital Elevation Models are the raw results of aerial photography. An orthophoto is a photograph that shows an object in its true orthographic position. The production of orthophotos involves a process that produces stereo images through image matching using various methods. In this research, area-based and feature-based image matching are performed. For area-based image matching, sample sizes of 9×9, 15×15, and 33×33 pixels and two correlation equations were used: Cross-correlation and Mean Relative Grey Difference. Image matching results between samples from the right and left photographs were compared based on the resulting correlation values. From the data processing, area-based image matching with a sample size of 33×33 gives the best results that high Cross-Correlation values and low MRGVD values indicate strong image matching accuracy, especially for images with many details and variations. Thus, the combination of cross-correlation and MRGVD metrics provides an effective and comprehensive analysis for image matching.

Three-dimensional Stereoscopic Evaluation and Quantitative Gray Scale Assessment of the Nasolabial Fold and Its Application in Injection Therapy.

With the growing demand for aesthetic enhancements, particularly filler injections for nasolabial folds (NLFs), there is a need for more objective and quantitative assessment methods beyond the traditional subjective scales like the Wrinkle Severity Rating Scale (WSRS). This study introduces a novel evaluation method combining 3-dimensional (3D) imaging and grayscale analysis to objectively assess NLF severity and treatment outcomes. A total of 100 healthy participants (86 females, 14 males; mean age 41.85±11.53 years) were assessed using WSRS scores and quantitative 3D measurements. The results demonstrated a significant positive correlation between WSRS scores and gray values (r=0.985, P < 0.05). Significant differences in gray values and 3D linear measurements were observed across WSRS score groups, with high intra-rater reliability confirmed by intraclass correlation coefficients. In addition, 54 patients (mean age 42.9±8.9 y) with moderate to severe NLFs received hyaluronic acid injections. One month post-treatment, WSRS scores significantly decreased (from 3.74±0.45 to 1.63±0.59, P < 0.05), and significant improvements were observed in several 3D measurements. The average gray value also decreased significantly (P < 0.05), indicating a reduction in wrinkle depth. These findings were supported by representative photos showing patient satisfaction and no severe complications. This study validates the use of 3D imaging and grayscale analysis as reliable tools for objectively assessing NLF severity and monitoring treatment efficacy, potentially improving the precision and reliability of aesthetic evaluations in clinical practice.

Risk assessment for pancreatic fistula by intraoperative image analysis of laparoscopic and robotic gastrectomy.

Pancreatic fistula (PF) is one of the most serious postoperative complications of gastrectomy. Misidentification of the boundary between the pancreas and the dissected fat is a primary concern. In this study, we focused on differences in the appearance of the pancreas and the dissected fat in actual surgical images and statistically analyzed the relationship between the pancreas and the dissected fat. We analyzed data from 109 gastric cancer patients who underwent curative gastrectomy between November 2018 and March 2023. Intraoperative images were taken from videos of lymph node dissections of Nos.6 and 8a regions, and the mean gray value of the areas was measured using ImageJ software for analysis. The visceral fat area (VFA) was evaluated by preoperative axial CT at the umbilical level using Ziostation software. A significant correlation was observed between the fat/pancreas gray value ratio in the No.8a lymph node region and the drain/serum amylase ratio (P < 0.001). The fat/pancreas gray value ratio in the No.6 lymph node region correlated with VFA (P < 0.001). The VFA and drain/serum amylase ratio were significantly higher in the group with intra-abdominal complications (P = 0.004). We revealed significant relationships between the fat/pancreas gray value ratio with drain/serum amylase and VFA. Detecting differences in gray values between the pancreas and the dissected fat may lead to a decrease in the drain/serum amylase ratio and PF.

A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy. Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures ( Portalbone ) and digitally reconstructed radiographs containing only bone structures ( DRRbone ) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtain Portalbone. Another threshold was applied to generate DRRbone when the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering the DRRbone with the Portalbone obtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy. Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0–127, 0–63 and 0–31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance. Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

Mammographically detected breast clustered microcalcifications localized by chest thin-section computed tomography

BackgroundTo explore the capability and clinical significance of chest thin-section computed tomography (CT) for localization of mammographically detected clustered microcalcifications.MethodsA total of 69 patients with 71 mammographically detected clustered microcalcifications received surgical biopsy under the guidance of mammography (MG), CT was used to localize calcifications combined with MG if calcifications can be seen on CT. Intraoperative mammography of the specimens were performed in all cases for identification of the resected microcalcifications. The clinical, imaging and pathological information of these patients were analyzed.ResultsA total of 42 (59.15%) cases of calcifications were localized by CT + MG, 29 (40.85%) cases were guided only by the mammography. All suspicious calcifications on the mammography were successfully removed. Pathological results showed 42 cases were cancer, 23 cases were benign, and 6 cases were atypical hyperplasia. The mean age in the CT + MG group was older than that of the MG group (54.12 vs. 49.27 years; P = 0.014). The maximum diameter of clusters of microcalcifications on mammography in the CT + MG group was larger than that of the MG group [(cranio-caudal view, 1.52 vs. 0.61 mm, P = 0.000; mediolateral oblique (MLO) view, 1.53 vs. 0.62 mm, P = 0.000)]. The gray value ratio (calcified area / paraglandular; MLO, P = 0.004) and the gray value difference (calcified area - paraglandular; MLO, P = 0.005) in the CT + MG group was higher than that of the MG group. Multivariate analysis showed that the max diameter of clusters of microcalcifications (MLO view) was a significant predictive factor of localization by CT in total patients (P = 0.001).ConclusionsAbout half of the mammographically detected clustered microcalcifications could be localized by thin-section CT. Maximum diameter of clusters of microcalcifications (MLO view) was a predictor of visibility of calcifications by CT. Chest thin-section CT may be useful for localization of calcifications in some patients, especially for calcifications that are only visible in one view on the mammography.

Research on identification of coal and gangue under complex working conditions based on relief feature

ABSTRACT To improve the identification accuracy of coal gangue under the complex working conditions of pulverized coal and moisture adhesion on the surface, this paper studies the distribution difference of characteristic curves under five working conditions of different proportions of moisture and pulverized coal, and finds that moisture has a greater influence on gradient features, while pulverized coal has a greater influence on gray texture features. Then this paper studies the distribution of coarse texture and fine texture in coal and gangue, and finds that there are many fine textures and few coarse textures on the surface of gangue, while coal shows the opposite law. In addition, the ratio of pixels with large gray value to pixels with small gray value can reduce the occurrence of noncompliance with the above laws. Finally, through the combination of coarse texture, fine texture, and gray value difference, the relief feature is put forward. Compared with different features, the average feature overlap ratio proposed in this paper is lower, at 23.83%. Compared with different methods, this method has a higher identification rate under different working conditions, and the average identification accuracy rate is 89.59%, which is at least 13.93% higher than other methods.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

Biomechanical modeling of rice seedling stalk based on multi-scale structure and heterogeneous materials

Rice seedling stalks are biologically characterized by multilayer porous structure and multiphase inhomogeneous material properties. This biological properties make it challenging to reveal the biomechanical behavior and damage mechanism of rice seedling stalks under mechanical load. In this research, the mechanical properties of the cell wall in each leaf sheath of rice seedling stalk were measured by nanoindentation, and a three-dimensional (3D) structure model of rice seedling stalk was established using micro-computed tomography (Micro-CT). According to the gray value difference in the CT images, rice seedling stalk was defined as three types of materials: protoplasts, parenchyma cell wall and collenchyma cell wall. A 3D finite element model of rice seedling stalk with heterogeneous material distribution was established based on the mapping relationship of gray value-density-elastic modulus. The accuracy of the model was verified by radial compression and axial tensile tests from three aspects: macroscopic mechanical response, microscopic mechanical behavior, and actual load effect. The results show that the modeling method is fast and reliable, and effectively improves the analytical accuracy of biomechanical properties for living plant materials with multi-layer porous structure characteristics, heterogeneous anisotropic material properties, and high moisture content. It also provides a useful tool for revealing the damage mechanism and quantitative damage evaluation of living plants under mechanical loading. This will be of great help to the optimization of the structure and working parameters of transplanting mechanism.

New technology using crystalline lens autofluorescence for presbyopia and cataract grading.

To investigate whether fundus autofluorescence (FAF) obtained using an ultra-wide field (UWF) fundus camera with an artificial opacity pattern can grade the degree of presbyopia and nuclear cataract. Sixty eyes of 30 patients were enrolled in this prospective diagnostic study. The nuclear cataract (nuclear color/opalescence (NC/NO)) was graded according to the Lens Opacity Classification System III. The monocular near point of accommodation (NPA) was measured in eyes with NC3/NO3 or less. The mean gray value difference between the central 8 artificial opacity lesions and peripheral 8 artificial opacity lesions in the retinal AF was measured. The correlation between the mean gray value difference, NPA, and nuclear cataract grade was analyzed. The mean nuclear cataract grade of 60 eyes was 3.2 ± 1.6 and mean NPA of 37 eyes was 45.3 ± 16.1 cm. The mean gray value differences increased with increasing nuclear cataract grade (eyes with NC/NO grade 1, 53.3 ± 11.4; 2, 78.3 ± 13.6; 3, 95.2 ± 12.2; 4, 101.6 ± 11.9; 5, 109.0 ± 22.9; and 6, 121.1 ± 12.0; p < 0.001). The mean gray value difference was positively correlated with both the monocular NPA (R2 = 0.637; β coefficient = 1.009; 95% CI, 0.748 to 1.271; p < 0.001) and nuclear cataract grade (R2 = 0.661; β coefficient = 12.437; 95% CI, 10.097 to 14.778; p < 0.001). The FAF camera with an artificial opacity pattern attached can be used to effectively diagnose the degree of presbyopia and nuclear cataract.

Application of polarization imaging in counting plywood layers by machine vision

This paper introduces a machine vision system based on polarization imaging, which is applicable for automatically counting the number of internal layers in plywood. Industrial machine vision usually suffers from a low accuracy due to low contrast and high complexity of the images, which could be overcome by the introduction of polarization imaging. A polarization camera was utilized to capture images with polarization angles of 0°, 45°, 90°, and 135°, and then a degree of polarization (DOP) distribution image was obtained by calculating the DOP for each pixel. Compared with the intensity distribution image, the contrast of the DOP distribution image was increased by about 60% and the excessive information in the image including wood’s natural texture, dirty spots, dicing marks, and artifacts was mostly filtered. A gray value difference algorithm was applied to the images to determine the edges of the internal layers of plywood and count them up automatically. The experimental results illustrated that polarization imaging could improve the counting accuracy of the algorithm effectively.

Research on Calculation Method of Radiation Response Eigenvalue of a Single-Chip Active Pixel Sensor.

In this paper, we present a calculation method for the radiation response eigenvalue based on a monolithic active pixel sensor. By comparing the statistical eigenvalues of different regions of a pixel array in bright and dark environments, the linear relationship between the statistical eigenvalues obtained by different algorithms and the radiation dose rate was studied. Additionally, a dose rate characterization method based on the analysis of the eigenvalues of the MAPS response signal was proposed. The experimental results show that in the dark background environment, the eigenvalues had a good linear response in the region of any gray value in the range of 10–30. In the color images, due to the difference in the background gray values in adjacent color regions, the radiation response signal in dark regions was confused with the image information in bright regions, resulting in the loss of response signal and affecting the analysis results of the radiation response signal. For the low dose rate radiation field, as the radiation response signal was too weak and there was background dark noise, it was necessary to accumulate frame images to obtain a sufficient response signal. For the intense radiation field, the number of response events in a single image was very high, and only two consecutive frames of image data needed to be accumulated to meet the statistical requirements. The binarization method had a good characterization effect for the radiation at a low dose rate, and the binarization processing and the total gray value statistics of the response data at a high dose rate could better characterize the radiation dose rate. The calibration experiment results show that the binarization processing method can meet the requirements of using a MAPS for wide-range detection.

Predictive value of image indexes of B-mode and power Doppler sonography on the efficacy of high intensity focused ultrasound ablation for uterine fibroids

Objective To investigate the value of the image indexes of B-mode and power Doppler sonography in predicting the therapeutic efficacy of high intensity focused ultrasound (HIFU) ablation for uterine fibroids. Materials and methods Two hundred and three patients with a solitary uterine fibroid were enrolled in this study. Every patient underwent transvaginal sonography (TVS) and magnetic resonance imaging (MRI) before HIFU. The patients were divided into hypointense, isointense and hyperintense fibroid groups based on T2 weighted MR imaging characteristics, and ultrasonic image indexes of the fibroids in different groups were compared. Multiple linear regression analysis was used to evaluate the correlation between ultrasonic image indexes and energy efficiency factor (EEF), non-perfused volume (NPV) ratio of uterine fibroids. Results Among them, 72 patients had a hypointense fibroid, 70 had an isointense fibroid and 61 had a hyperintense fibroid. Significant differences were observed in the ultrasound imaging gray scale value difference between the myometrium and uterine fibroids (GSmyo-fib), the ultrasound imaging gray scale value ratio of fibroids over the myometrium (GSfib/myo), and the ratio of power Doppler pixel area to fibroid area (PDPA/FA) among the three groups (p < 0.05). Linear regression analysis showed that the PDPA/FA and the location of fibroids were the factors affecting the NPV ratio, a model for predicting the NPV ratio was established. Conclusions A model with the PDPA/FA for NPV ratio could be used to predict the therapeutic efficacy of HIFU for fibroids.

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

Aflatoxin B1 (AFB1) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB1 is of great significance to ensure food safety. In this study, a novel method for classification of AFB1 in single maize kernels was developed. Four groups of maize kernel samples with different AFB1 concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB1 were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500–1000 nm) and long wave near-infrared (LWNIR) region (1000–2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500–2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB1 concentration classification of single maize kernels.

SAR Image Segmentation Based on Fisher Vector Superpixel Generation and Label Revision

This paper addresses the problem of superpixel-bases segmentation of synthetic aperture radar (SAR) images. Most superpixel segmentation methods have difficulties in segmenting adjacent regions with similar gray values, due to only considering spatial and gray information. To solve this problem and improve segmentation accuracy, this paper proposes a SAR image segmentation method based on Fisher vector superpixel generation and label revision. Firstly, the Fisher vector is obtained by processing the Gaussian mixture function. By introducing the Fisher vector, a distance formula is constructed for superpixel segmentation. Therefore, the adjacent regions with similar gray values can be segmented effectively in the generated superpixel map. Secondly, the superpixels are clustered using the K-means algorithm to obtain the initial label map. Then, with extracted edge information as constraints, the pixel labels obtained by K-means are repaired pixel by pixel to get the middle label map, according to the number and gray value difference of labels. This overcomes influence of noise generated by K-means. Finally, the middle label map is relabeled using the region growth algorithm to divide pixel blocks. Isolated pixel blocks surrounded by similar labels are corrected, based on the gray mean difference. The final label result has a better segmentation accuracy. Experiments on synthetic SAR images and real images demonstrate that the proposed algorithm achieves higher segmentation accuracy than six state-of-the-art clustering algorithms for SAR image segmentation.

An Enhancement and Detection Method for a Glue Dispensing Image Based on the CycleGAN Model

During the active alignment focusing process of car camera assembly, lenses and holders need to be gummed by creamy white and translucent UV glue. The quality of glue dispensing can directly influence the performance of car cameras. Because of the translucency of UV glue, the glue dispensing image may present a low contrast situation, which increases the difficulty of vision detection. This paper proposes a method based on CycleGAN to enhance the glue dispensing image and effectively overcome the problems of blurred and low contrast edges. First, the glue part of the image is segmented into twenty regions. Second, the VGG16 model is used to divide the abovementioned twenty regions into high-contrast images and low-contrast images. Next, the CycleGAN model is trained to enhance the low-contrast images, and then convert them to high-contrast images. Finally, glue contours are extracted by using thresholding segmentation and edge detection to ensure that the quality of glue dispensing can be detected. The success rates of the VGG16 model and the CycleGAN model are 96% and 58%, respectively. The results show that the proposed method can effectively enhance the low contrast part of the glue region and improve the detection accuracy. Specifically, it can increase the gray value difference between the glue and the background from 20 to 55, while the background is substantially retained. The detailed information of the edges of the images is enriched. The accuracy of glue edge extraction can be increased to 99%, which is an approximately 75% improvement compared to the methods without enhancement.

Study on Ash Gray and Ash Condensation Index Evaluation during Combustion of Medium Cigarettes

The color of cigarette paper directly affect the ash color and ash condensation quality of cigarettes. The ash color and ash condensation quality of cigarettes with black cigarette paper are studied by using the self-developed cigarette paper gray and ash condensation quality measuring equipment and taking black as the background color in this paper. The effect of black cigarette paper on the ash gray is discussed in the combustion process of black cigarette paper for the first time. The results show that the average grey value of black cigarette paper is 116.59, and the uneven grey value is mainly due to the non-uniformity of cigarette paper among different batches and the cut tobacco structure of medium cigarettes, on which the shooting from 3 different angles has a great impact. It is appropriate to calculate the grey and ash quality by taking the average value of 3-side shooting. Black background board is ideal for testing cigarette paper gray. The gray value difference of randomly selected cigarettes is too large, so it is necessary to measure in batches, and the measurement accuracy of gray value can be guaranteed only if the sample quantity is sufficient. The maximum deviation of gray value photographed by different cameras of the same cigarette is 18.17, which is 15.6% of the average value, and the average value of ash coagulation index is 90.20. The cigarette paper ash detection equipment used in this paper is accurate, which can reflect the cigarette ash performance comprehensively and objectively, and such an equipment has rich detective means, and has high automation and reliability in the analysis of detection results. It is necessary to take photos from different angles in the measurement process to ensure the accuracy of gray measurement value of a cigarette to the largest extent, as the samples showed significant differences.

Visible and infrared missile-borne image registration based on improved SIFT and joint features

For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.

Molecularly imprinted electrospun fiber membrane for colorimetric detection of hexanoic acid

Abstract An imprinted electrospun fiber membrane was developed for the detection of volatile organic acids, which are key components of human body odor. In this study, hexanoic acid (HA) was selected as the target, polymethyl methacrylate (PMMA) was used as the substrate, and colorimetric detection of HA was achieved by a bromocresol purple (BCP) chromogenic agent. The results showed that the morphology of the fiber membrane was uniform and continuous, and it showed excellent selectivity and specificity to HA. Photographs of the color changes before and after fiber membrane adsorption were recorded by a camera and quantified by ImageJ software by the difference in gray value (ΔGray). This method is simple, intuitive, and low cost and has great potential for application in human odor analysis.

Shedding Damage Detection of Metal Underwater Pipeline External Anticorrosive Coating by Ultrasonic Imaging Based on HOG + SVM

Underwater pipelines are the channels for oil transportation in the sea. In the course of pipeline operation, leakage accidents occur from time to time for natural and man-made reasons which result in economic losses and environmental pollution. To avoid economic losses and environmental pollution, damage detection of underwater pipelines must be carried out. In this paper, based on the histogram of oriented gradient (HOG) and support vector machine (SVM), a non-contact ultrasonic imaging method is proposed to detect the shedding damage of the metal underwater pipeline external anti-corrosion layer. Firstly, the principle of acoustic scattering characteristics for detecting the metal underwater pipelines is introduced. Following this, a HOG+SVM image-extracting algorithm is used to extract the pipeline area from the underwater ultrasonic image. According to the difference of mean gray value in the horizontal direction of the pipeline project area, the shedding damage parts are identified. Subsequently, taking the metal underwater pipelines with three layers of polyethylene outer anti-corrosive coatings as the detection object, an Autonomous Surface Vehicle (ASV) for underwater pipelines defect detection is developed to verify the detection effect of the method. Finally, the underwater ultrasonic image which used to detect the metal underwater pipeline shedding damage is obtained by acoustic sensor. The results show that the shedding damage can be detected by the proposed method. With the increase of shedding damage width, the effect of pipeline defect location detection is better.

Bridge Vibration Virtual Sensor Based on Eulerian Video Magnification and Gray Mean Difference

A non-contact measurement method is proposed to overcome the shortcomings of traditional contact sensors in this paper. The camera is used as a non-contact virtual sensor of the whole field vibration. The motion in raw video was amplified by Eulerian Video Magnification technology and the gray mean of the selected region from processed video is obtained. The gray value difference between frames represents the dynamic response signal of the local area of the bridge structure under the excitation. The actual displacement amplitude in the time domain of the target structure can be obtained by conversion. A model bridge experiment was carried on, the results show that good coincidence in time domain and good accuracy in frequency domain, the error of the proposed method is 2.2% compared with the traditional measurement, indicating that the method without manual mark has the advantages of accurate, easy to obtain, low cost and high efficiency.