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Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing
摘要： Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.
关键词： crack detection,image processing,signal processing,percolation,Non-contact sensing
更新于2025-09-23 15:22:29
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Multichannel fiber laser acoustic emission sensor system for crack detection and location in accelerated fatigue testing of aluminum panels
摘要： Detection and location of the source of acoustic emission in a thin aluminum panel is demonstrated using a multichannel fiber laser sensor system. Acoustic emission generated by a crack in an aluminum panel used as a test coupon in an accelerated fatigue experiment is detected and the location of the crack identified. Acoustic emission is detected over a bandwidth of around 0.5 MHz from a serially multiplexed array of four laser sensors and compared with measurements taken from four piezo-electric sensors co-located with the fiber laser sensors. The location of the crack is determined by first estimating time difference of arrival of signals at each sensor using a novel algorithm based on the cumulative distribution transform method with hyperbolic positioning. The fiber laser sensor is shown to match the signal-to-noise ratio of the industry standard (Mistras S9225) piezo-electric acoustic emission sensor.
关键词： location,crack detection,fiber laser,acoustic emission,accelerated fatigue testing
更新于2025-09-23 15:21:01
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Road Surface Crack Detection using a Light Field Camera
摘要： During traditional road surveys, inspectors capture images of pavement surface using cameras that produce 2D images, which can then be automatically processed to get a road surface condition assessment. This paper proposes a novel crack detection system that uses a light field imaging sensor, notably the Lytro Illum camera, instead of a conventional 2D camera, to capture road surface light field images. Light field images capture the light rays originating from different directions, thus providing a richer representation of the observed scene. The proposed system explores the disparity information, which can be computed from the light field, to obtain information about cracks observable in the pavement images. A simple processing system is considered, to show the potential use of this type of sensors for crack detection. Encouraging experimental crack detection results are presented based on a set of road pavement light field images captured over different pavement surface textures. A performance comparison with a state-of-the-art 2D image crack detection system is included, confirming the potential of using this type of sensors.
关键词： Light field imaging,image processing,road crack detection
更新于2025-09-19 17:15:36
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Deep learning method for detection of structural microcracks by Brillouin scattering based distributed optical fiber sensors
摘要： Brillouin scattering (BS)–based distributed optical fiber sensors (DOFS) provide distributed sensing capabilities by monitoring the strain along entire segments of structures. Large cracks, such as those with large crack opening displacements (COD) can be detected by strain peaks or singularities along the measurement length of distributed sensors. Microcracks do not provide visible pronounced local peaks along the length of measured distributed strains. The peaks corresponding to microcracks are submerged within the measurement noise due to low signal-to-noise ratio (SNR) of BS systems. Deep learning (DL) methods have the potential to automatically extract feature representations from data exhibiting lower SNRs, and improve the crack detection sensitivity of the BS-based DOFS. Development of the proposed DL method includes construction of model architecture, design of a training algorithm and the detection process. A 15-m-long wide-flange steel beam with artificial defects is built and employed in this study. A comprehensive experimental program is undertaken in order to train, validate and test the generality of the proposed DL method. Experimental results demonstrate that the DL method is capable of extracting highly discernable microcrack features from the distributed strains, and distinguish the crack-induced local peaks from the noise. Microcracks with CODs as small as 23 microns are accurately detected in the present work.
关键词： Structural health monitoring,optical fiber sensors,Brillouin Scattering,crack detection,deep learning
更新于2025-09-19 17:13:59
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Object-Based Crack Detection and Attribute Extraction From Laser-Scanning 3D Profile Data
摘要： Cracks in 3D pavement data often show poor continuity, low contrast and different depths, which bring great challenges to related application. Recently, crack attributes, e.g. depth and width have attracted attention of highway agencies for maintenance decision-makings, but few studies have been conducted on crack attributes. This paper presents object-based image analysis (OBIA) method for crack detection and attribute extraction from laser-scanning 3D pro?le data with elevation accuracy about 0.25 mm. Firstly, a high-pass ?lter designed for pavement components in our previous research was applied to remove the ?uctuation posture in 3D data, and then the smallest of-constant false-alarm rate algorithm was used to acquire lower point sets, including crack seeds and lower textures. Secondly, the objects were represented by above obtained 3D point sets and OBIA, especially, the depth statistics, shape and topological features of objects were described. Moreover, to enhance crack objects and remove texture objects gradually, multi-scale object selections and merges were conducted according to the local statistical characteristics differences of objects. Thirdly, the objects’ orientation attributes were combined with tensor voting to connect and infer ?nal crack objects, and then the object-level crack depth attributes could be extracted. The experimental results demonstrated that proposed method achieved average buffered Hausdorff scores of 94.39, Recall of 0.92 and F-value of 0.91 for crack detection on 30 real measured 3D asphalt pavement data. Furthermore, crack depth attributes can be extracted at different scales according requirements, the obtained location and depth attributes provide more comprehensive information for pavement maintenances.
关键词： Laser-scanning 3D,crack detection,crack attribute,tensor voting,OBIA
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Methods for Long-Distance Crack Location and Detection of Concrete Bridge Structures
摘要： In order to improve the efficiency of crack detection of concrete bridge structures, a new method based on computer vision technology and coordinate mapping is proposed. In this research, this crack measurement system is integrated mainly with a high magnification image acquisition system, a two-dimensional electric cradle head device and a laser ranging system. It has a set of observing coordinate system. Firstly, the marking points’ image coordinates are mapped to the observation coordinates. Secondly, according to the marking points’ observation coordinates, the measured crack’s coordinates are mapped to a same world coordinates so as to realize the spatial location of the measured cracks regardless of different test cycles or instrument’s setup positions, which is a great convenience for the review detection of surface cracks of concrete bridge structures. The experiments show that this method is efficient and convenient. It can automatically locate the measured cracks within 16 s, and the deviation is not more than ± 0.07 °. At a distance of 100 m, the measurement accuracy of crack width is better than ± 0.12 mm.
关键词： long-distance,crack location,computer vision,spatial coordinate mapping,crack detection,concrete bridge structures
更新于2025-09-11 14:15:04
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[IEEE 2019 IEEE International Conference on Image Processing (ICIP) - Taipei, Taiwan (2019.9.22-2019.9.25)] 2019 IEEE International Conference on Image Processing (ICIP) - Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized L <sub/>p</sub> Norm
摘要： Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However, the detection and segmentation of cracks on electroluminescence (EL) images of mono- or polycrystalline solar modules is a challenging task. In this work, we propose a weakly supervised learning strategy that only uses image-level annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells. We use a modified ResNet-50 to derive a segmentation from network activation maps. We use defect classification as a surrogate task to train the network. To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification. In addition, we provide a study how different parameterizations of the normalized Lp layer affect the segmentation performance. This approach shows promising results for the given task. However, we think that the method has the potential to solve other weakly supervised segmentation problems as well.
关键词： solar cell,weakly supervised semantic segmentation,EL imaging,crack detection,normalized Lp norm
更新于2025-09-11 14:15:04
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Handbook of Advanced Non-Destructive Evaluation || Induction Thermography of Surface Defects
摘要： A survey on theory, characteristic quantities, and the experimental technique of induction thermography is given. Induction thermography is used for surface defect detection in forged parts of ferromagnetic steel at typical frequencies of 100–300 kHz. Values for the detection limits for various types of cracks and approaches to determine crack depths are given. The sensitivity for crack detection is comparable to magnetic particle inspection. A hidden defect in ferritic steel with a coverage of 140 μm was detected by lowering the induction frequency down to 1500 Hz. Cracks in silicon solar cells were detected. Defects of ?bers were detected in carbon ?ber reinforced polymer (CFRP). Inductive excitation is complementary to ?ash excitation. Crack detection in railway components like rails and wheels was shown. In rails, a larger defect could be detected from a test car moving at a speed of up to 15 km/h. A fully automated demonstrator for wheel testing was built up, which can detect surface defects in railway wheels with sensitivity comparable to magnetic particle testing. Standardization of thermography has gained progress in the last years and led to ?rst standards on active thermography and induction thermography.
关键词： surface defect detection,induction thermography,crack detection,carbon fiber reinforced polymer,railway component testing,ferromagnetic steel
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Crack Detection and Images Inpainting Method for Thai Mural Painting Images
摘要： Thailand frescoes are an important art heritage in the world. However, the erosion of history has resulted in the color loss, stain and scratches of many mural paintings. How to repair the Thailand murals has become an urgent problem. It is an important scientific problem to use computer image inpainting technology to simulate and eliminate the missing pixels in the murals and obtain beautiful and intact murals. In this paper, a computer aided semi-automatic repair framework is proposed by combining a scratch detection procedure and a model optimization based inpainting procedure. To this end, we propose a scratch semi-automatic detection method. In this method, a small number of seed points are given by users, and the location of scratches is then computed by region growing method and morphological operation. After that, the pixel filling and color restoration in the missing region can be obtained by using different variational inpainting methods. The experiment shows that the proposed method is effective.
关键词： seed region growing,thai mural painting,images inpainting,images restroration,crack detection
更新于2025-09-10 09:29:36
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An Enhanced Time-Reversal Imaging Algorithm-Driven Sparse Linear Array for Progressive and Quantitative Monitoring of Cracks
摘要： A Lamb wave and linear piezoelectric lead zirconate titanate (PZT) array-based monitoring method for the detection and quantification of crack damage is presented in this paper. Because existing PZT array arrangements are not suitable for quantitative monitoring of crack damage both in orientation and in length, a sparse linear PZT array is introduced and applied to collect crack reflections. Based on this new array, a method for estimating crack orientation is proposed. An amplitude spectrum as a function of angle is mapped using time delayed and summed signals. By finding the peaks in the spectra, the central actuator element and corresponding orientation angle are determined. Furthermore, the time of flight imaging method is modified to display and evaluate cracks quantitatively. Validating experiments are conducted on a T6061 aluminum plate, monitoring and evaluating single and connected cracks with various orientations in different locations. As suggested by the experiments, the orientation of most cracks can be well recognized and all cracks can be quantitatively displayed by the proposed methods.
关键词： sensor arrays,waveguide theory,monitoring,Crack detection,signal processing
更新于2025-09-10 09:29:36